Longitudinal Survey

panel survey
repeated-measures survey
longitudinal panel study
wave survey

1940s (panel survey tradition); longitudinal designs codified mid-20th century

Established tradition; formalized in social science by Paul Lazarsfeld and colleagues (1940s panel studies)

A longitudinal survey collects structured questionnaire data from the same individuals or units at two or more distinct points in time. By tracking the same respondents across waves, researchers can distinguish genuine change from stable individual differences, establish temporal ordering between variables, and model trajectories of attitudes, behaviors, or outcomes in ways that a single cross-sectional snapshot cannot support.

Quantitative / mixed-methods survey design

Structured questionnaire responses collected from the same sample at multiple time points

survey panel-study cohort-study cross-sectional-survey diary-method experience-sampling-method


Longitudinal Test-Retest Reliability

longitudinal stability reliability
repeated-measurement reliability
temporal stability across waves
longitudinal retest coefficient

1904 (test-retest); longitudinal application formalized mid-20th century

Spearman, Charles; extended to longitudinal contexts by psychometric theorists

Longitudinal test-retest reliability quantifies how consistently a scale or measure performs across two or more time points in a longitudinal study. It extends the classic test-retest paradigm by accounting for planned, often substantive, time lags between waves — making it essential for validating instruments used in panel, cohort, or growth-curve research.

Reliability estimation / temporal stability

Repeated ordinal or continuous measurements across two or more time points

Nunnally, J. C. & Bernstein, I. H. (1994). Psychometric Theory (3rd ed.). McGraw-Hill.

MacKenzie, S. B., Podsakoff, P. M. & Podsakoff, N. P. (2011). Construct measurement and validation procedures in MIS and behavioral research: Integrating new and existing techniques. MIS Quarterly, 35(2), 293–334.


Longitudinal Thematic Analysis

LTA
longitudinal TA
repeated thematic analysis
temporal thematic analysis

2000s–2010s (formalized alongside longitudinal qualitative research methods)

Built on Braun & Clarke (2006) thematic analysis; longitudinal adaptation developed across qualitative health and social science research communities

Longitudinal Thematic Analysis (LTA) extends standard thematic analysis to data collected at multiple time points from the same participants or contexts. Rather than producing a single cross-sectional account, LTA maps how themes emerge, persist, transform, or disappear over time, enabling researchers to understand change, continuity, and process in qualitative terms. It is widely used in health, education, and social science research where lived experience unfolds over months or years.

Repeated-wave interview transcripts, diary data, focus group transcripts collected at multiple time points

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.

Sikveland, R. O., & Stokoe, E. (2019). Longitudinal qualitative research: Recurring patterns and change. Qualitative Research, 19(3), 314–328.

thematic-analysis reflexive-thematic-analysis longitudinal-qualitative-research narrative-analysis framework-analysis interpretive-phenomenological-analysis


Longitudinal Visual Analysis

LVA
longitudinal visual research
temporal visual analysis
repeated visual analysis

1970s–2000s (consolidated with digital methods in 2000s)

Developed across visual sociology and visual ethnography traditions; key contributions from Gillian Rose, Sarah Pink, and Howard Becker

Longitudinal Visual Analysis (LVA) is a qualitative research design that systematically collects, organises, and interprets visual data — photographs, video, maps, or diagrams — gathered at two or more time points to document change, continuity, or transformation in people, places, or social phenomena. By anchoring analysis to the temporal dimension of images, LVA goes beyond what a single-moment visual study can reveal, making visible patterns of development or decay that are otherwise invisible in a snapshot.

Photographs, video footage, diagrams, maps, archival images collected or reviewed at multiple time points

Rose, G. (2016). Visual Methodologies: An Introduction to Researching with Visual Materials (4th ed.). Sage.

Pink, S. (2007). Doing Visual Ethnography: Images, Media and Representation in Research (2nd ed.). Sage.

visual-ethnography photovoice content-analysis narrative-analysis documentary-analysis thematic-analysis


Longitudinal Web Scraping

repeated web scraping
time-series web data collection
longitudinal crawling
panel web scraping

Emergent practice in computational social science; formalized across internet research community

Longitudinal web scraping is a data collection technique that uses automated scripts to extract content from websites at multiple, predefined time points. By revisiting the same web sources repeatedly, researchers build a time-series dataset that captures how online content, prices, discourse, or behavior evolves. It is widely used in computational social science, economics, political science, health research, and digital humanities to study change without relying on retrospective self-report.

Structured and semi-structured web content (HTML, text, metadata) collected at multiple time points

Salganik, M. J. (2018). Bit by Bit: Social Research in the Digital Age. Princeton University Press.

Luscombe, A., Dick, K., & Walby, K. (2022). Algorithmic thinking in the public interest: navigating technical, legal, and ethical challenges in government web scraping. Quality & Quantity, 56(3), 1781–1802.

web-scraping longitudinal-survey api-based-data-collection longitudinal-api-based-data-collection sensor-data-collection content-analysis


Longstaff-Schwartz Method

Longstaff-Schwartz Least-Squares Monte Carlo

Francis A. Longstaff and Eduardo S. Schwartz

The Longstaff-Schwartz method (2001) is a Monte Carlo algorithm for pricing American options and Bermudan swaptions by approximating the optimal exercise boundary via least-squares regression. It has become the industry standard for pricing path-dependent derivatives where analytical solutions do not exist.

Longstaff, F. A., & Schwartz, E. S. (2001). Valuing American options by simulation: A simple least-squares approach. Review of Financial Studies, 14(1), 113-147.

Clements, D. J., & Minca, A. (2008). A simulation approach to estimating near-optimal valuation functions for Bermudan options. Journal of Computational Finance, 12(2), 73-96.

bates-model local-volatility sabr-model risk-neutral-valuation


LOPCOW

LOPCOW (LOgarithmic Percentage Change-driven Objective Weighting) is a weight objective multi-criteria decision-making (MCDM) method introduced by Ecer, F., Pamučar, D. in 2022. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Logarithmic percentage change variance-based objective weighting

Ecer, F., Pamučar, D. (2022). A novel LOPCOW-DOBI integrated sustainability performance evaluation methodology: An application in developing country banking sector. Omega

ahpsort aploco aras aroman artasi cobra cocoso codas


LOPM

LOPM (LoPM — Limits on Property Method) is a ranking multi-criteria decision-making (MCDM) method introduced by Farag, M. M. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Property-limit satisfaction scoring for material selection

Farag, M. M. (2020). Materials and process selection for engineering design. CRC Press


LoRA and PEFT

Low-Rank Adaptation and Parameter-Efficient Fine-Tuning

LoRA ve PEFT — Parametre Verimli İnce Ayar

LoRA (Low-Rank Adaptation), introduced by Hu et al. in 2022, and the broader family of parameter-efficient fine-tuning (PEFT) methods adapt large pretrained language models to new tasks by training only a small number of extra parameters instead of every weight in the model. This makes fine-tuning possible with far less GPU memory and compute while leaving the original model largely untouched.

Parameter-efficient fine-tuning of large pretrained models

Hu, E. J. et al. (2022). LoRA: Low-Rank Adaptation of Large Language Models. ICLR.

Lester, B. et al. (2021). The Power of Scale for Parameter-Efficient Prompt Tuning. EMNLP.

vision-transformer variational-autoencoder generative-adversarial-network random-forest xgboost


Loss Distribution Model

Severity-Frequency Model
Aggregate Loss Model
Claim Size Distribution Model
Hasar Dağılımı Modeli

A Loss Distribution Model is a parametric statistical framework used in actuarial science to characterise the probabilistic behaviour of insurance claim amounts and frequencies. Developed comprehensively by Klugman, Panjer, and Willmot in their foundational text Loss Models: From Data to Decisions (first edition 1998, fourth edition 2012), these models underpin premium rating, reserving, reinsurance pricing, and regulatory capital calculations across the insurance and risk-management industries.

Klugman, S. A., Panjer, H. H., & Willmot, G. E. (2012). Loss Models: From Data to Decisions (4th ed.). Wiley.


Bibliometric Laws: Lotka, Bradford, Zipf

Bibliometric Laws: Lotka's Law, Bradford's Law, and Zipf's Law

bibliometric distributions
productivity laws
frequency laws
information science laws

Alfred J. Lotka, Samuel C. Bradford, George K. Zipf

Three foundational empirical laws describe the structure and distribution of scientific information: Lotka's Law characterizes author productivity (most authors publish few papers; a few publish many), Bradford's Law describes journal concentration (a small number of core journals contain the majority of papers on a topic), and Zipf's Law models word and term frequency (word frequency inversely proportional to its rank). These regularities, discovered in the mid-20th century, are remarkably robust across disciplines and have become essential tools for understanding research productivity, organizing information resources, and designing search strategies.

Lotka, A. J. (1926). The frequency distribution of scientific productivity. Journal of the Washington Academy of Sciences, 16(12), 317–323.

Bradford, S. C. (1934). Sources of information on specific subjects. Engineering, 137, 85–86.

Zipf, G. K. (1949). Human Behavior and the Principle of Least Effort. Addison-Wesley.


LVQOL

The Low Vision Quality of Life Questionnaire (LVQOL) is a comprehensive instrument designed to measure the multidimensional impact of significant vision loss on health-related quality of life in individuals with low vision. Developed by Wolffsohn and Cochrane (2000), the LVQOL incorporates functional, emotional, social, and psychological domains and emphasizes measurement of disability and coping in populations with moderate-to-severe vision impairment where adaptive rehabilitation is the primary intervention.

Wolffsohn, J. S., & Cochrane, A. L. (2000). Design of the low vision quality-of-life questionnaire (LVQOL) and measurement of its item and scale validity and reliability. Optometry Vis Sci, 77(3), 144-152.

Cochrane, A. L., & Wolffsohn, J. S. (2008). Testing the LVQOL with age-related macular degeneration using RASCH analysis. Invest Ophthalmol Vis Sci, 49(11), 4971-4978.

nei-vfq-25 impact-vision-impairment amd-quality-of-life visual-function-index


Lower Extremity Functional Scale

Jill M. Binkley, Paul W. Stratford, Sheila A. Lott, Duane L. Riddle

The Lower Extremity Functional Scale (LEFS) is a 20-item patient self-report instrument designed to assess functional limitations in individuals with lower extremity musculoskeletal disorders. Developed by Binkley, Stratford, Lott, and Riddle in 1999 and published in Physical Therapy, the LEFS provides a validated, general lower-extremity outcome measure applicable across diverse pathologies (knee, ankle, hip, foot injuries and conditions), making it particularly valuable in physical therapy and rehabilitation settings.

Binkley JM, Stratford PW, Lott SA, Riddle DL. The Lower Extremity Functional Scale (LEFS): scale development, measurement properties, and clinical application. Phys Ther. 1999;79(4):371-383.

patient-specific-functional-scale global-rating-of-change-scale faos ikdc-subjective-knee-form


LPF-EDAS

LPF-EDAS (LPF-CRITIC-EDAS — Linguistic Pythagorean Fuzzy EDAS with CRITIC weighting (Akram-Ramzan-Deveci 2023)) is a ranking multi-criteria decision-making (MCDM) method introduced by Akram, M., Ramzan, N., Deveci, M. in 1995 crisp; 2023 variant applicator. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Linguistic Pythagorean fuzzy ranking — LPFN (I_ψ, I_ζ) with ψ²+ζ² ≤ τ²

Akram, M., Ramzan, N., Deveci, M. (2023). Linguistic Pythagorean fuzzy CRITIC-EDAS method for multiple-attribute group decision analysis. Engineering Applications of Artificial Intelligence


LSDVC

Bias-Corrected Least Squares Dummy Variable (LSDVC)

Önyargı Düzeltilmiş En Küçük Kareler Kukla Değişken Tahmincisi

LSDVC is a bias-corrected panel data estimator introduced by Kiviet (1995) to address the well-known Nickell bias that afflicts the standard Least Squares Dummy Variable (LSDV) estimator in dynamic panel models with a lagged dependent variable. It is particularly suited for researchers working with datasets where the number of time periods T is small relative to the number of cross-sectional units N, such as firm-level or country-level panels spanning a short time horizon.

Kiviet, J. F. (1995). On bias, inconsistency, and efficiency of various estimators in dynamic panel data models. Journal of Econometrics, 68(1), 53–78.


LSTM

recurrent neural network with memory cells

LSTM (Long Short-Term Memory) is a recurrent neural network architecture, introduced by Sepp Hochreiter and Jürgen Schmidhuber in 1997, that can learn long-term dependencies in sequential data and is widely used for time-series and sequence prediction. It keeps an internal memory that lets information persist across many time steps.

Recurrent neural network (gated memory cell)

Time-series forecasting & sequence prediction

Hochreiter, S. & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.

transformer-nlp cnn-classification autoencoder random-forest xgboost


Lumped Capacitance Method

Lumped Capacitance Method for Transient Heat Conduction

lumped mass analysis
lumped system analysis

The Lumped Capacitance Method is a simplification technique for solving unsteady-state heat transfer problems. It assumes that thermal properties are uniform throughout a solid body and that temperature variations within the object are negligible. This approach enables engineers to solve complex transient heat conduction problems using ordinary differential equations rather than partial differential equations.

Carslaw, H. S., & Jaeger, J. C. (1959). Conduction of Heat in Solids. Oxford University Press.

Incropera, F. P., DeWitt, D. P., Bergman, T. L., & Lavine, A. S. (2007). Fundamentals of Heat and Mass Transfer (6th ed.). Wiley.


Lumsdaine-Papell Test

Lumsdaine-Papell Unit-Root Test with Two Breaks

Lumsdaine-Papell İki Kırılmalı Birim Kök Testi

The Lumsdaine-Papell test, introduced by Robin Lumsdaine and David Papell in 1997, extends the Zivot-Andrews single-break unit-root test to allow for two simultaneous structural breaks in the intercept and/or linear trend of a time series. It is widely used in macroeconomics and finance when data are suspected to have experienced two major regime shifts — such as policy changes, financial crises, or wars — and the researcher needs to determine whether the series is nonetheless integrated of order one.

Models AA, AB, CC (intercept and/or trend breaks)

Lumsdaine, R. L., & Papell, D. H. (1997). Multiple trend breaks and the unit-root hypothesis. Review of Economics and Statistics, 79(2), 212–218.


Lysholm Knee Scale

The Lysholm Knee Scoring Scale is an 8-item knee outcome instrument developed by Swedish orthopedic surgeons Lysholm and Gillquist in 1982 to evaluate knee ligament surgery results. Published in the American Journal of Sports Medicine, the Lysholm Scale was among the first validated knee outcome measures and remains widely used in clinical practice and orthopedic research, particularly in studies of anterior cruciate ligament injuries and knee ligament reconstruction.

Patient self-report and functional testing

Lysholm J, Gillquist J. Evaluation of knee ligament surgery results with special emphasis on use of a scoring scale. Am J Sports Med. 1982;10(3):150-154.

ikdc-subjective-knee-form patient-specific-functional-scale global-rating-of-change-scale faos


M-Estimator

m-estimation
huber regression
robust m-regression
M-Tahmin Ediciler

M-estimators are a robust generalisation of maximum likelihood estimation, formalised in the work of Peter J. Huber (Huber & Ronchetti, 2009). Instead of squaring every residual, they apply a bounded loss function so that large residuals from outliers are down-weighted rather than allowed to dominate the fit.

M-estimation (minimises a robust loss via weight functions)

Huber, P. J., & Ronchetti, E. M. (2009). Robust Statistics (2nd ed.). Wiley.

Maronna, R. A., Martin, R. D., Yohai, V. J., & Salibián-Barrera, M. (2019). Robust Statistics: Theory and Methods (with R) (2nd ed.). Wiley.

mm-estimator least-trimmed-squares quantile-regression ols-regression ridge-regression


MABAC

MABAC (Multi-Attributive Border Approximation area Comparison) is a ranking multi-criteria decision-making (MCDM) method introduced by Pamučar, D., Ćirović, G. in 2015. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Border approximation area (distance from BAA)

Pamučar, D., Ćirović, G. (2015). The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC). Expert Systems with Applications

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


MACBETH

MACBETH (Measuring Attractiveness by a Categorical-Based Evaluation Technique) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Bana e Costa, C. A., Vansnick, J.-C. in 1994. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Weight_Subjective (qualitative pairwise judgment, linear programming, MAVT-based)

Bana e Costa, C. A., Vansnick, J.-C. (1994). MACBETH — An interactive path towards the construction of cardinal value functions. International Transactions in Operational Research


Machine Learning-Assisted Chromatin Immunoprecipitation Sequencing Peak Calling

AI-assisted ChIP-seq peak identification

2008 (classical); ML-assisted variants 2012–present

Building on MACS (Zhang et al. 2008); ML extensions by Haiminen et al. and others (2010s–2020s)

Machine learning-assisted ChIP-seq peak calling extends classical statistical peak detection with supervised or unsupervised learning models that distinguish genuine protein-binding sites from background noise. By training on sequence composition, read coverage profiles, and epigenomic features, these methods improve sensitivity and specificity compared with threshold-based approaches, particularly in low-signal or heterogeneous chromatin contexts.

Supervised/unsupervised ML-augmented peak detection pipeline

ChIP-seq read alignments (BAM/BED), control input DNA, genome reference

Kharchenko, P. V., Tolstorukov, M. Y., & Park, P. J. (2008). Design and analysis of ChIP-seq experiments for DNA-binding proteins. Nature Biotechnology, 26(12), 1351-1359.

Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., Myers, R. M., Brown, M., Li, W., & Liu, X. S. (2008). Model-based analysis of ChIP-Seq (MACS). Genome Biology, 9(9), R137.

chip-seq-peak-calling rna-seq-differential-expression epigenome-wide-association-study variant-calling sequence-alignment single-cell-rna-seq-analysis


Machine learning-assisted copy number variation analysis

Machine Learning-Assisted Copy Number Variation Analysis

ML-CNV analysis
ML-based CNV calling
machine learning CNV detection
deep learning CNV analysis

Multiple groups; notable early ML-CNV tools include CNV-RF (Bellos et al., 2014) and CANOES (Backenroth et al., 2014)

Machine learning-assisted CNV analysis applies supervised, unsupervised, or deep learning algorithms to detect genomic regions that are duplicated or deleted relative to a reference genome. Rather than relying on fixed statistical thresholds, ML models learn discriminative patterns from read-depth signals, allele frequencies, and other features, substantially improving sensitivity and specificity over classical tools — especially in noisy or low-coverage sequencing data.

Supervised/unsupervised machine learning pipeline for genomic structural variant detection

Whole-genome sequencing (WGS), whole-exome sequencing (WES), SNP array intensities, read-depth profiles

Aganezov, S., Goodwin, S., Sherman, R. M., Sedlazeck, F. J., Mehta, G., Rushbrook, S., ... & Schatz, M. C. (2020). Comprehensive analysis of structural variants in breast cancer genomes using single-molecule sequencing. Genome Research, 30(9), 1258-1273.

Zare, F., Dow, M., Monteleone, N., Bhatt, A., & Bhatt, D. L. (2017). An evaluation of copy number variation detection tools for cancer using whole exome sequencing data. BMC Bioinformatics, 18(1), 286.


Machine learning-assisted epigenome-wide association study

Machine Learning-Assisted Epigenome-Wide Association Study

epigenome-wide association study with machine learning

2010s (methodological consolidation ~2015–2020)

Teschendorff, Relton, and others in the epigenomics field

Machine learning-assisted EWAS integrates conventional epigenome-wide association testing with machine learning models to identify DNA methylation sites associated with a phenotype of interest. By combining the statistical rigour of EWAS with the pattern-recognition power of algorithms such as elastic net, random forest, or gradient boosting, this approach handles the extreme dimensionality of methylation arrays (450,000–850,000 CpG sites) more effectively than univariate testing alone, and can capture non-linear and interaction effects that standard linear models miss.

DNA methylation arrays (e.g., Illumina 450K or EPIC), phenotype labels, covariate matrices

Teschendorff, A. E., & Relton, C. L. (2018). Statistical and integrative system-level analysis of DNA methylation data. Nature Reviews Genetics, 19(3), 129–147.

Jones, M. J., Goodman, S. J., & Kobor, M. S. (2015). DNA methylation and healthy human aging. Aging Cell, 14(6), 924–932.

genome-wide-association-study dna-methylation-analysis epigenetic-clock random-forest lasso-regression differential-methylation-analysis


Machine learning-assisted expression quantitative trait loci analysis

Machine Learning-Assisted Expression Quantitative Trait Loci Analysis

ML-assisted eQTL analysis
ML eQTL mapping
deep learning eQTL
predictive eQTL modeling

2015 (key ML-eQTL methods; foundational eQTL work: Jansen & Nap 2001)

Gamazon et al. (PrediXcan), Zhou & Troyanskaya (DeepSEA); broader field ca. 2015-onward

Machine learning-assisted eQTL analysis integrates supervised learning models — ranging from elastic-net regression to deep neural networks — into the classical eQTL framework to predict and map genetic variants that regulate gene expression. By training predictive models on reference panels (e.g., GTEx), the approach enables imputation of gene expression in cohorts lacking RNA data, substantially increasing statistical power and enabling trans-tissue generalisation.

Genotype arrays or whole-genome sequencing, RNA-seq expression data, reference panels

Gamazon, E. R., Wheeler, H. E., Shah, K. P., Mozaffari, S. V., Aquino-Michaels, K., Carroll, R. J., ... & Im, H. K. (2015). A gene-based association method for mapping traits using reference transcriptome data. Nature Genetics, 47(9), 1091-1098.

Zhou, J., & Troyanskaya, O. G. (2015). Predicting effects of noncoding variants with deep learning-based sequence model. Nature Methods, 12(10), 931-934.


Machine Learning-Assisted Gene Set Enrichment Analysis

ML-GSEA
deep learning pathway enrichment
neural GSEA
ML-assisted pathway analysis

2005 (GSEA); ML integration from ~2015 onward

Subramanian et al. (GSEA foundation, 2005); various ML extensions thereafter

Machine learning-assisted gene set enrichment analysis (ML-GSEA) extends the classical GSEA framework by incorporating supervised or unsupervised ML models — such as random forests, neural networks, or deep learning architectures — to improve the detection, ranking, and biological interpretation of enriched gene sets from high-throughput expression data. The approach is particularly valuable for complex, non-linear gene-set relationships that classical enrichment statistics may miss.

Computational enrichment analysis with machine learning

Gene expression matrices, ranked gene lists, pathway/gene set databases

Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550.

Ma, J., Yu, M. K., Fong, S., Ono, K., Sage, E., Demchak, B., Sharan, R., & Ideker, T. (2018). Using deep learning to model the hierarchical structure and function of a cell. Nature Methods, 15(4), 290–298.


Machine learning-assisted genome-wide association study

Machine Learning-Assisted Genome-Wide Association Study

ML-GWAS
machine learning GWAS
AI-assisted GWAS
deep learning GWAS

Multiple groups; popularized through integrations such as Listgarten et al. (2012) and Novembre & Stephens (2008); ML augmentation formalized ~2015-2020

Machine learning-assisted GWAS integrates classical genome-wide association testing with machine learning models to improve the detection of genetic variants associated with complex traits. Where traditional GWAS tests each single nucleotide polymorphism (SNP) independently using linear or logistic regression, ML-GWAS captures non-linear interactions and epistasis, ranks candidate loci more accurately, and reduces the false discovery burden in large biobank datasets. The approach has become increasingly prominent as sample sizes and genomic complexity outpace the assumptions of conventional single-SNP tests.

Genome-wide SNP arrays, whole-genome sequencing data, phenotype data

Beam, A. L., & Kohane, I. S. (2018). Big data and machine learning in health care. JAMA, 319(13), 1317-1318.

Szymanski, M., Holland-Letz, T., & Kneib, T. (2022). Machine learning approaches to GWAS: methods, pitfalls, and applications. Briefings in Bioinformatics, 23(3), bbac068.

genome-wide-association-study polygenic-risk-score random-forest deep-learning snp-annotation linkage-disequilibrium-analysis


Machine Learning-Assisted Metabolomics Analysis

metabolite profiling with machine learning

2000s–2010s (rapid adoption 2015–present)

Convergent development; foundational reviews by Liebal et al. (2020) and earlier multivariate metabolomics work by Trygg, Holmes, and Nicholson

Machine learning-assisted metabolomics analysis is an integrative bioinformatics pipeline that couples untargeted or targeted metabolite profiling — via mass spectrometry or NMR — with supervised and unsupervised ML algorithms to discover biomarkers, classify phenotypes, and model metabolic states. By handling the extreme dimensionality and collinearity inherent in metabolomics datasets (hundreds to thousands of features, tens to hundreds of samples), ML methods such as random forests, support vector machines, and neural networks extract biologically interpretable patterns that classical univariate statistics routinely miss.

High-dimensional metabolite abundance matrices (LC-MS, GC-MS, NMR spectra)

Liebal, U. W., Phan, A. N. T., Sudhakar, M., Raman, K., & Blank, L. M. (2020). Machine learning applications for mass spectrometry-based metabolomics. Metabolites, 10(6), 243.

Bylesjö, M., Rantalainen, M., Cloarec, O., Nicholson, J. K., Holmes, E., & Trygg, J. (2006). OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification. Journal of Chemometrics, 20(8-10), 341-351.


Machine Learning-Assisted Microbiome Diversity Analysis

ML-based microbiome analysis
supervised microbiome diversity
microbiome ML classification
ML-driven alpha/beta diversity analysis

2011–2016 (formalization of ML integration into microbiome pipelines)

Pasolli, Segata and colleagues (meta-ML framework); broader field grew from Turnbaugh et al. human microbiome work

Machine learning-assisted microbiome diversity analysis integrates classical alpha and beta diversity metrics with supervised or unsupervised ML models to classify host phenotypes, identify discriminant taxa, and uncover community-level signatures from 16S rRNA or shotgun metagenomic data. It extends traditional diversity analysis beyond descriptive statistics toward predictive and explanatory modelling across health, ecology, and environmental science.

Computational pipeline (supervised/unsupervised ML + diversity metrics)

16S rRNA amplicon sequencing or shotgun metagenomic count tables; OTU/ASV abundance matrices

Pasolli, E., Truong, D. T., Malik, F., Waldron, L., & Segata, N. (2016). Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights. PLOS Computational Biology, 12(7), e1004977.

Wirbel, J., Pyl, P. T., Kartal, E., Zych, K., Kashani, A., Milanese, A., ... & Zeller, G. (2019). Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer. Nature Medicine, 25(4), 679–689.


Machine Learning-Assisted Pathway Enrichment Analysis

ML-assisted PEA
ML-based pathway analysis
machine learning pathway enrichment
ML-enhanced gene set enrichment

Multiple groups; early integration of ML with PEA circa 2010s (e.g., Ma'ayan Lab, Greene Lab)

Machine learning-assisted pathway enrichment analysis integrates classical statistical pathway enrichment methods — such as over-representation analysis or gene set enrichment analysis — with machine learning algorithms to improve sensitivity, handle high-dimensional omics data, and uncover non-linear biological patterns. The approach moves beyond ranking pathways by p-value alone, using ML models to weight gene contributions, distinguish signal from noise across many samples, and prioritize biologically meaningful pathways in complex datasets.

Computational pipeline combining statistical enrichment with machine learning

Transcriptomics (RNA-seq, microarray), proteomics, metabolomics, multi-omics gene lists

Chen, E. Y., Tan, C. M., Kou, Y., Duan, Q., Wang, Z., Meirelles, G. V., Clark, N. R., & Ma'ayan, A. (2013). Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics, 14, 128.

Way, G. P., & Greene, C. S. (2018). Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencoders. Pacific Symposium on Biocomputing, 23, 80–91.

gene-set-enrichment-analysis over-representation-analysis random-forest principal-component-analysis multi-omics-integration network-based-enrichment-analysis


Machine Learning-Assisted Phylogenetic Analysis

ML-based phylogenetics
deep learning phylogenetics
neural network tree inference
ML phylogenomics

2000s–2020s (active development phase 2018–present)

Multiple contributors; early applications by Kolaczkowski & Thornton (2004) for model selection; deep learning formulations by Suvorov et al. (2020) and Zou et al. (2020)

Machine learning-assisted phylogenetic analysis integrates supervised, unsupervised, or deep learning models into the evolutionary tree inference workflow to improve speed, accuracy, or scalability beyond what classical maximum-likelihood and Bayesian methods achieve alone. Applications range from substitution model selection and tree topology prediction to placement of novel sequences onto existing reference trees and detection of recombination or horizontal gene transfer events.

Multiple sequence alignments (DNA, RNA, protein); phylogenomic matrices

Nesterenko, L., et al. (2024). Machine learning methods in phylogenetics: A review of applications and perspectives. Briefings in Bioinformatics, 25(1), bbad441.

Suvorov, A., Hochuli, J., & Schrider, D. R. (2020). Accurate inference of tree topologies from multiple sequence alignments using deep learning. Systematic Biology, 69(2), 221–233.

maximum-likelihood-phylogenetics bayesian-phylogenetics multiple-sequence-alignment molecular-clock-analysis ancestral-sequence-reconstruction genome-wide-association-study


Machine Learning-Assisted RNA-seq Differential Expression Analysis

ML-based DE analysis
deep learning RNA-seq DE
neural network differential expression
ML-augmented transcriptomics

Multiple groups; scVI (Lopez et al., 2018) and DCA (Eraslan et al., 2019) are landmark tools

Machine learning-assisted RNA-seq differential expression analysis augments classical statistical DE testing (DESeq2, edgeR, limma-voom) with ML models — including neural networks, random forests, and variational autoencoders — to better handle the high dimensionality, zero-inflation, and batch effects inherent in RNA-seq count data. The approach improves feature selection, noise reduction, and detection power, especially in large or complex experimental designs.

Raw or normalized RNA-seq count matrices (bulk or single-cell)

Lopez, R., Regier, J., Cole, M. B., Jordan, M. I., & Yosef, N. (2018). Deep generative modeling for single-cell transcriptomics. Nature Methods, 15(12), 1053–1058.

Eraslan, G., Simon, L. M., Mircea, M., Mueller, N. S., & Theis, F. J. (2019). Single-cell RNA-seq denoising using a deep count autoencoder. Nature Communications, 10(1), 390.

rna-seq-differential-expression single-cell-rna-seq-analysis gene-set-enrichment-analysis pathway-enrichment-analysis deep-learning random-forest


Machine Learning-Assisted Sequence Alignment

ML-guided alignment
deep learning sequence alignment
neural sequence alignment
AI-assisted MSA

2010s–2020s (deep learning era, accelerating post-2017)

Multiple contributors; notable milestones include Llinares-López et al. (DEDAL, 2023) and Jumper et al. (AlphaFold MSA module, 2021)

Machine learning-assisted sequence alignment uses statistical learning models — including deep neural networks and protein language models — to compute biologically meaningful alignments between nucleotide or amino acid sequences. By learning substitution patterns and structural constraints from large training corpora, these methods surpass classical scoring matrices (e.g., BLOSUM, PAM) in sensitivity for remote homologs and structurally constrained regions, making them the current state of the art for difficult alignment tasks in genomics and proteomics.

Computational pipeline / supervised and self-supervised learning

Nucleotide or amino acid sequences (FASTA/FASTQ), pre-trained protein language model embeddings

Llinares-López, F., Berthet, Q., Blondel, M., Teboul, O., & Vert, J.-P. (2023). Deep embedding and alignment of protein sequences. Nature Methods, 20(1), 104–111.

Jumper, J., Evans, R., Pritzel, A., et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583–589.

multiple-sequence-alignment pairwise-sequence-alignment hidden-markov-model-profile protein-structure-prediction sequence-homology-search phylogenetic-analysis


Machine learning-assisted single-cell RNA-seq analysis

Machine Learning-Assisted Single-Cell RNA Sequencing Analysis

ML-scRNA-seq
deep learning scRNA-seq
AI-assisted scRNA-seq
ML-guided single-cell transcriptomics

2015-2018 (rapid expansion with scVI 2018, Seurat v3 2019)

Nir Yosef, Fabian Theis, and colleagues (scVI/scANVI framework; broader community-driven)

Machine learning-assisted single-cell RNA sequencing (scRNA-seq) analysis integrates supervised, unsupervised, and deep generative models into the standard scRNA-seq workflow to handle the unique challenges of single-cell data: extreme sparsity, high dimensionality, technical noise, and batch effects across experiments. Methods such as variational autoencoders (scVI), graph neural networks, and transfer learning substantially improve cell-type identification, trajectory inference, and cross-study data integration compared with purely statistical approaches.

Single-cell RNA-seq count matrices (UMI or read counts per cell per gene)

Lopez, R., Regier, J., Cole, M. B., Jordan, M. I., & Yosef, N. (2018). Deep generative modeling for single-cell transcriptomics. Nature Methods, 15(12), 1053-1058.

Luecken, M. D., & Theis, F. J. (2019). Current best practices in single-cell RNA-seq analysis: a tutorial. Molecular Systems Biology, 15(6), e8746.


Machine Learning-Assisted Genomic Variant Calling

ML-based variant calling
deep learning variant detection
AI-assisted variant detection
neural network variant calling

2018 (DeepVariant publication); broader ML approaches from ~2014 onward

Ryan Poplin, Mark DePristo and colleagues at Google Brain / Verily (DeepVariant)

Machine learning-assisted variant calling uses statistical learning models — most notably convolutional neural networks — to distinguish genuine genomic variants (SNPs, indels) from sequencing artifacts in aligned short- or long-read data. Unlike heuristic callers that rely on hand-crafted filters, ML-based approaches learn directly from large labeled datasets of validated variants, improving sensitivity and specificity across diverse sequencing platforms and coverage depths. Google's DeepVariant (2018) is the landmark implementation that brought deep learning into mainstream variant calling.

Aligned sequencing reads (BAM/CRAM), reference genome (FASTA)

Poplin, R., Chang, P. C., Alexander, D., Schwartz, S., Colthurst, T., Ku, A., Newburger, D., Dijamco, J., Nguyen, N., Afshar, P. T., Gross, S. S., Dorfman, L., McLean, C. Y., & DePristo, M. A. (2018). A universal SNP and small-indel variant caller using deep neural networks. Nature Biotechnology, 36(10), 983–987.

Krusche, P., Trigg, L., Boutros, P. C., Mason, C. E., De La Vega, F. M., Moore, B. L., Gonzalez-Porta, M., Eberle, M. A., Tezak, Z., Lababidi, S., Truty, R., Asimenos, G., Funke, B., Fleharty, M., Salit, M., Goldfeder, R. L., & Zook, J. M. (2019). Best practices for benchmarking germline small-variant calls in human genomes. Nature Biotechnology, 37(5), 555–560.

whole-genome-sequencing read-alignment base-quality-score-recalibration variant-annotation haplotype-phasing structural-variant-detection


Machine Learning-Augmented Causal Impact Analysis

ML-augmented causal impact
ML-CausalImpact
machine learning causal impact
ML-augmented BSTS

Brodersen et al. (foundational BSTS framework, 2015); Chernozhukov et al. (double ML augmentation, 2018)

Machine learning-augmented causal impact analysis combines quasi-experimental counterfactual reasoning with flexible ML prediction models to estimate the causal effect of an intervention on a time series outcome. Building on Brodersen et al.'s Bayesian structural time series (BSTS) framework and extended by double/debiased ML methods, it constructs a synthetic counterfactual from donor covariates and infers the treatment effect as the gap between observed and predicted post-intervention outcomes.

Quasi-experimental causal inference with ML

Time series; panel data; observational longitudinal data

Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 9(1), 247-274.

Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1-C68.

causal-impact-analysis synthetic-control-method difference-in-differences interrupted-time-series doubly-robust-estimation panel-event-study


Machine Learning-Augmented Coarsened Exact Matching Estimator

ML-augmented CEM
ML-CEM
automated coarsened exact matching
ML-assisted CEM

Extension of Iacus, King & Porro (2012) CEM; ML integration developed in subsequent causal ML literature

Machine Learning-Augmented Coarsened Exact Matching extends Coarsened Exact Matching (Iacus, King & Porro, 2012) by using supervised machine learning to automate and optimise the coarsening step — the discretisation of continuous covariates into bins — rather than relying on researcher-specified cutpoints. This reduces both ad hoc subjectivity in coarsening decisions and residual imbalance, while preserving CEM's core logic of exact matching within coarsened strata.

Observational cross-sectional or panel data with continuous or categorical covariates

Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.

Imai, K., & Ratkovic, M. (2014). Covariate balancing propensity score. Journal of the Royal Statistical Society: Series B, 76(1), 243-263.


Machine Learning-Augmented Counterfactual Impact Evaluation

ML-augmented counterfactual evaluation
ML-CIE
causal ML impact evaluation
double ML counterfactual evaluation

Machine learning-augmented counterfactual impact evaluation combines the credibility of potential-outcomes causal inference with the flexibility of modern ML algorithms. Rather than imposing parametric functional forms for confounders, ML learners — such as lasso, random forests, or neural nets — estimate nuisance functions (propensity scores, outcome regressions) that are then used to construct approximately unbiased estimates of causal effects. The canonical instantiation is Double/Debiased Machine Learning (DML), formalized by Chernozhukov et al. (2018).

Causal inference / ML-augmented evaluation

Panel, cross-sectional, or time-series observational data

Athey, S., & Imbens, G. W. (2019). Machine learning methods that economists should know about. Annual Review of Economics, 11, 685-725.

counterfactual-impact-evaluation double-debiased-machine-learning causal-impact-analysis synthetic-control-method difference-in-differences propensity-score-matching


Machine Learning-Augmented Difference-in-Differences Estimator

ML-DiD
double/debiased ML DiD
DML difference-in-differences
augmented DiD

Chernozhukov et al. (double/debiased ML framework); Sant'Anna & Zhao (2020) for DR-DiD

Machine learning-augmented DiD combines the classic difference-in-differences identification strategy with flexible ML estimators for nuisance functions — the propensity score and the outcome regression — to obtain valid causal estimates even when treatment selection and outcome dynamics are complex, high-dimensional, or nonlinear. The approach, rooted in double/debiased machine learning (Chernozhukov et al., 2018) and doubly-robust DiD (Sant'Anna & Zhao, 2020), guards against misspecification bias while preserving the core DiD logic of before-after, treated-versus-control comparisons.

Panel or repeated cross-sections with continuous, binary, or count outcomes

Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-Differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.


Machine learning-augmented doubly robust estimation

Machine Learning-Augmented Doubly Robust Estimation

ML-DR
AIPW with ML
Double/Debiased ML doubly robust
DML-DR

Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey & Robins

Machine learning-augmented doubly robust (ML-DR) estimation combines the classical doubly robust (AIPW) identification strategy with flexible machine learning models for the nuisance functions — the propensity score and the outcome regression. The result is a causal estimator that is consistent if either ML component is correctly specified, and that achieves valid, root-n inference even when the nuisance models are estimated with high-dimensional regularisation or nonparametric learners.

Semiparametric causal estimator with ML nuisance

Cross-sectional or panel; continuous, binary, or count outcome

Farrell, M. H., Liang, T., & Misra, S. (2021). Deep Neural Networks for Estimation and Inference. Econometrica, 89(1), 181-213.


Machine Learning-Augmented Entropy Balancing for Causal Inference

ML-EB
augmented entropy balancing
ML-augmented EB
doubly-robust entropy balancing

Hainmueller (2012) for entropy balancing; ML augmentation developed by Zhao & Percival (2017) and subsequent literature

Machine learning-augmented entropy balancing (ML-EB) combines Hainmueller's entropy balancing reweighting scheme with a machine-learning outcome model to produce a doubly-robust causal estimator. By jointly optimising covariate balance weights and a flexible predicted-outcome adjustment, ML-EB delivers consistent treatment-effect estimates even when either the weighting or the outcome model is misspecified, and it handles high-dimensional covariate spaces that classical entropy balancing cannot easily balance.

Observational cross-sectional or panel data with binary treatment

Hainmueller, J. (2012). Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. Political Analysis, 20(1), 25-46.

Zhao, Q., & Percival, D. (2017). Entropy balancing is doubly robust. Journal of Causal Inference, 5(1), 20160010.

entropy-balancing propensity-score-matching inverse-probability-weighting doubly-robust-estimation targeted-maximum-likelihood-estimation covariate-balancing-propensity-score


Machine Learning-Augmented Event Study Design

ML-augmented event study
high-dimensional event study
DML event study
causal ML event study

Chernozhukov et al. (double/debiased ML foundation); applied to event studies in subsequent econometrics literature

Machine learning-augmented event study design combines the standard event study framework — which traces outcome dynamics around a treatment date — with ML-based methods such as double/debiased machine learning (DML) or regularized regression to handle high-dimensional covariates, improve confounder control, and produce valid causal estimates when the covariate space is too large for conventional regression to manage reliably.

Panel data or repeated cross-sections with high-dimensional covariates

Athey, S., & Imbens, G. W. (2022). Design-based analysis in difference-in-differences settings with staggered adoption. Journal of Econometrics, 226(1), 62-79.

event-study-design double-debiased-machine-learning difference-in-differences causal-forest panel-event-study dynamic-difference-in-differences


Machine Learning-Augmented Fuzzy Regression Discontinuity Design

ML-augmented fuzzy RDD
ML fuzzy RD
double ML fuzzy RDD
nonparametric fuzzy RDD

2001 (fuzzy RDD); 2018 (double ML augmentation)

Hahn, Todd & Van der Klaauw (fuzzy RDD); Chernozhukov et al. (ML augmentation framework)

ML-augmented fuzzy RDD extends the classical fuzzy regression discontinuity design by replacing parametric polynomial approximations with flexible machine learning estimators. Where standard fuzzy RDD uses IV-style estimation at a threshold with imperfect compliance, the ML-augmented variant leverages nonparametric learners — such as random forests or neural networks — to model both the outcome and the first-stage treatment probability near the cutoff, reducing misspecification bias while preserving causal identification.

Cross-sectional or panel data with a continuous running variable and imperfect compliance

Hahn, J., Todd, P., & Van der Klaauw, W. (2001). Identification and estimation of treatment effects with a regression-discontinuity design. Review of Economic Studies, 68(1), 201-209.

Semenova, V., & Chernozhukov, V. (2021). Debiased machine learning of conditional average treatment effects and other causal functions. The Econometrics Journal, 24(2), 264-289.


Machine Learning-Augmented Instrumental Variables Estimation

ML-IV
MLIV
Double/Debiased ML with IV
DML-IV

Belloni, Chernozhukov & Hansen; Chernozhukov et al.

Machine learning-augmented instrumental variables combines the causal identification power of classical IV with modern high-dimensional machine learning — using methods such as LASSO, random forests, or neural networks to select valid instruments and model nuisance functions, thereby improving first-stage fit and enabling valid inference even when the number of potential instruments or controls is large relative to the sample size.

Causal inference / semi-parametric estimation

Observational panel or cross-sectional data with many potential instruments

Belloni, A., Chen, D., Chernozhukov, V., & Hansen, C. (2012). Sparse models and methods for optimal instruments with an application to eminent domain. Econometrica, 80(6), 2369-2429.

instrumental-variables double-debiased-machine-learning lasso-regression two-stage-least-squares propensity-score-matching causal-forest


Machine Learning-Augmented Interrupted Time Series Analysis

ML-ITS
ML-augmented ITS
machine learning ITS
causal ML interrupted time series

Brodersen et al. (2015); Varian (2014) — foundational ML-for-causal-inference literature

Machine Learning-Augmented Interrupted Time Series (ML-ITS) estimates the causal effect of a discrete intervention by training a machine learning model on pre-intervention time series data, projecting a counterfactual trajectory into the post-intervention period, and measuring the gap between observed and predicted outcomes. It extends classical ITS by replacing parametric trend assumptions with flexible ML estimators such as gradient boosting, random forests, or Bayesian structural time-series models.

Quasi-experimental causal inference with ML counterfactual

Univariate or multivariate time series with a known intervention point

Varian, H. R. (2014). Big Data: New Tricks for Econometrics. Journal of Economic Perspectives, 28(2), 3-28.


Machine Learning-Augmented Inverse Probability Weighting Estimator

ML-IPW
nonparametric IPW
data-adaptive IPW
ML-augmented propensity weighting

Hirano, Imbens & Ridder (semiparametric foundation, 2003); Chernozhukov et al. (DML framework, 2018)

Machine learning-augmented inverse probability weighting replaces parametric logistic regression with flexible ML algorithms to estimate treatment propensity scores, then reweights the sample to balance treated and control units. By leveraging data-adaptive learners such as lasso, random forests, or gradient boosting, ML-IPW controls for high-dimensional and nonlinear confounders that classical IPW misses, while retaining the intuitive weighting framework.

Cross-sectional or panel; continuous/binary outcome; binary or multi-valued treatment

Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient estimation of average treatment effects using the estimated propensity score. Econometrica, 71(4), 1161-1189.


Machine Learning-Augmented Marginal Structural Model with Flexible Nuisance Estimation

ML-MSM
ML-augmented MSM
data-adaptive MSM
TMLE-MSM

2000 (MSM); 2011 (ML-augmented via targeted learning)

Robins, Hernan & Brumback (MSM, 2000); van der Laan & Rose (ML augmentation, TMLE framework, 2011)

The machine learning-augmented marginal structural model combines the causal rigour of Robins et al.'s MSM framework with flexible, data-adaptive ML algorithms for estimating propensity scores and outcome models. By replacing parametric nuisance models with ensemble learners or neural networks, ML-MSMs recover valid causal estimates under confounding without relying on correctly specified parametric forms.

Causal inference / semiparametric weighted regression

Longitudinal or cross-sectional observational data with time-varying or static treatments

Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.

Luedtke, A. R., & van der Laan, M. J. (2016). Statistical inference for the mean outcome under a possibly non-unique optimal treatment strategy. Annals of Statistics, 44(2), 713-742.


Machine Learning-Augmented Matching Estimator for Causal Inference

ML-augmented matching
ML matching estimator
high-dimensional matching estimator
data-adaptive matching estimator

Abadie & Imbens (classical matching); Chernozhukov et al. (ML augmentation framework)

The machine learning-augmented matching estimator combines classical nearest-neighbor or propensity-score matching with ML algorithms — such as lasso, random forests, or gradient boosting — to select covariates, estimate propensity scores, and correct for residual bias. The result is a matching-based causal estimator that remains valid under high-dimensional confounding where traditional hand-specified matching fails.

Causal inference / nonparametric matching

Cross-sectional or panel; continuous/binary outcome; high-dimensional covariates

Abadie, A., & Imbens, G. W. (2006). Large sample properties of matching estimators for average treatment effects. Econometrica, 74(1), 235-267.


Machine Learning-Augmented Panel Event Study Estimator

ML-augmented event study
ML event study
panel event study with ML
machine learning event study

Chernozhukov, Wuthrich & Zhu; Freyaldenhoven, Hansen & Shapiro (parallel developments)

The machine learning-augmented panel event study extends the classical panel event study by replacing or augmenting parametric counterfactual models with machine learning estimators — such as LASSO, random forests, or matrix completion — to construct more accurate pre-event baselines, detect violations of parallel trends, and produce valid causal effect estimates across multiple post-event periods.

Balanced or unbalanced panel data with multiple pre- and post-event periods

Chernozhukov, V., Wuthrich, K., & Zhu, Y. (2021). An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls. Journal of the American Statistical Association, 116(536), 1849-1864.

Freyaldenhoven, S., Hansen, C., & Shapiro, J. M. (2019). Pre-event Trends in the Panel Event-Study Design. American Economic Review, 109(9), 3307-3338.

difference-in-differences synthetic-control panel-fixed-effects double-lasso causal-forests event-study


Machine Learning-Augmented Placebo Test

Machine Learning-Augmented Placebo Test for Causal Identification

ML placebo test
data-driven placebo falsification
ML-augmented falsification test
ML permutation placebo

Chernozhukov, Hansen, and collaborators; Athey and Imbens

The machine learning-augmented placebo test is a causal-inference validation technique that uses flexible ML estimators — such as causal forests, LASSO, or double/debiased ML — to conduct falsification checks on an identification strategy. By replacing real treatment assignments with placebo (fake) assignments and verifying that the estimated effect collapses to zero, researchers confirm that their causal findings are not artefacts of model misspecification or confounding.

Panel, cross-sectional, or observational high-dimensional data

Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1-C68.

Athey, S., & Imbens, G. W. (2019). Machine learning methods that economists should know about. Annual Review of Economics, 11, 685-725.

difference-in-differences synthetic-control-method instrumental-variables regression-discontinuity-design causal-forest double-debiased-machine-learning


Machine Learning-Augmented Propensity Score Matching Estimator

ML-PSM
boosted propensity score matching
ML-augmented PSM
nonparametric propensity score matching

McCaffrey, Ridgeway & Morral (2004); Westreich, Lessler & Funk (2010)

Machine learning-augmented propensity score matching (ML-PSM) replaces the traditional logistic regression used to estimate propensity scores with flexible machine learning algorithms — such as gradient boosted trees, random forests, or LASSO — to better capture complex, nonlinear relationships among covariates. The resulting richer propensity scores improve covariate balance and reduce bias in the estimated average treatment effect on the treated (ATT).

Observational cross-sectional or panel data; binary treatment; continuous or binary outcome

McCaffrey, D. F., Ridgeway, G., & Morral, A. R. (2004). Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychological Methods, 9(4), 403-425.

Westreich, D., Lessler, J., & Funk, M. J. (2010). Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression. Journal of Clinical Epidemiology, 63(8), 826-833.


Machine Learning-Augmented Propensity Score Weighting

nonparametric propensity score weighting

Lee, Lessler & Stuart (2010); Chernozhukov et al. (2018, DML framework)

Machine learning-augmented propensity score weighting (ML-PSW) replaces logistic regression with flexible ML algorithms — such as gradient boosting, LASSO, or random forests — to estimate the propensity score, then uses inverse probability weights to balance treated and control groups. This reduces model-misspecification bias when the true relationship between covariates and treatment assignment is complex or high-dimensional.

Causal inference / semiparametric weighting

Observational panel or cross-sectional data with binary or multi-valued treatment

Lee, B. K., Lessler, J., & Stuart, E. A. (2010). Improving propensity score weighting using machine learning. Statistics in Medicine, 29(3), 337-346.


Machine Learning-Augmented Regression Discontinuity Design

ML-RDD
ML-augmented RD
data-adaptive RDD
nonparametric RDD with ML

Imbens & Wager (2019); Calonico, Cattaneo & Farrell (2019)

Machine learning-augmented regression discontinuity design (ML-RDD) combines the sharp identification logic of classical RDD — exploiting a known assignment cutoff in a running variable — with flexible, data-adaptive ML methods for bandwidth selection, conditional mean estimation, and covariate adjustment. The goal is to recover a more accurate and less assumption-laden estimate of the local average treatment effect at the threshold.

Observational cross-section or panel with a continuous running variable and a known cutoff

Calonico, S., Cattaneo, M. D., & Farrell, M. H. (2019). Optimal mean squared error bandwidth selection for regression discontinuity designs. Bernoulli, 25(4A), 2703-2729.

Imbens, G., & Wager, S. (2019). Optimized regression discontinuity designs. Review of Economics and Statistics, 101(2), 264-278.


Machine Learning-Augmented Sensitivity Analysis for Causality

Machine Learning-Augmented Sensitivity Analysis for Causal Inference

ML-augmented sensitivity analysis
ML sensitivity analysis for causality
machine learning sensitivity analysis
debiased ML sensitivity analysis

Cinelli & Hazlett (sensitivity framework); Chernozhukov et al. (ML augmentation for causal estimation)

Machine learning-augmented sensitivity analysis combines flexible ML estimators with formal robustness checks to assess how much unmeasured confounding would be required to overturn a causal finding. Rooted in Chernozhukov et al.'s double/debiased ML framework and Cinelli and Hazlett's omitted-variable-bias sensitivity tools, it delivers both high-dimensional covariate adjustment and transparent communication of remaining uncertainty about unobserved confounders.

Sensitivity analysis / causal robustness assessment

Observational panel or cross-sectional data with treatment and outcome variables

Cinelli, C., & Hazlett, C. (2020). Making sense of sensitivity: extending omitted variable bias. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 82(1), 39-67.

difference-in-differences instrumental-variables propensity-score-matching regression-discontinuity double-debiased-machine-learning synthetic-control


Machine Learning-Augmented Synthetic Control Method

ML-augmented SCM
augmented synthetic control
ASC
penalized synthetic control

The machine learning-augmented synthetic control method extends the classical synthetic control estimator by using penalized regression or other ML algorithms — such as lasso, ridge, or random forests — to construct the donor weights and to model pre-treatment outcome trajectories. The augmentation corrects for residual imbalance left by the standard weighting step, yielding lower bias when no perfect synthetic control exists.

Ben-Michael, E., Feller, A., & Rothstein, J. (2021). The augmented synthetic control method. Journal of the American Statistical Association, 116(536), 1789-1803.

Abadie, A. (2021). Using synthetic controls: Feasibility, data requirements, and methodological aspects. Journal of Economic Literature, 59(2), 391-425.


Machine Translation

MT
neural machine translation
automatic translation
Makine Çevirisi (Machine Translation)

Machine translation (MT) is a natural-language-processing task that automatically converts text in one language into another. Modern MT is built on neural sequence-to-sequence models — the attention mechanism introduced by Bahdanau et al. (2015) and the transformer architecture of Vaswani et al. (2017) — and it widens access to sources for multilingual data analysis and research.

Neural sequence-to-sequence (attention / transformer)

Input: Source-language text

Output: Target-language text

Attention (Bahdanau et al., 2015); Transformer self-attention (Vaswani et al., 2017)

Bahdanau, D., Cho, K. & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. International Conference on Learning Representations (ICLR).

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. & Polosukhin, I. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems (NeurIPS).


Maclaurin Symmetric Mean Operator

Variants developed from Maclaurin's mathematical theory

The Maclaurin Symmetric Mean (MSM) operator is an aggregation method that combines multiple criteria or attribute values using symmetric mean functions. Unlike simple averaging, MSM captures interactions between criteria and enables flexible sensitivity to criterion magnitudes through a parameter λ. It is particularly useful in fuzzy multi-criteria decision analysis and handles both individual and joint effects of criteria.

Symmetric mean aggregation operator for multiple criteria

Qin, J., Liu, X., & Pedrycz, W. (2014). An extended TOPSIS model for multiple attribute decision making with interval-valued intuitionistic fuzzy information. International Journal of Fuzzy Systems, 16(1), 99-113.

Bonferroni, C. (1950). Sulle medie di potenze. Giornale dell'Istituto Italiano degli Attuari, 13, 37-48.

bonferroni-mean power-mean weighted-geometric-mean fuzzy-aggregation topsis


MACONT

MACONT (Mixed Aggregation by Comprehensive Normalization Technique) is a ranking multi-criteria decision-making (MCDM) method introduced by Wen, Z. Liao, H. Zavadskas, E. K. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Hybrid aggregation combining multiple normalization methods to reduce bias

Wen, Z., Liao, H., Zavadskas, E. K. (2020). MACONT: Mixed Aggregation by Comprehensive Normalization Technique for Multi-Criteria Analysis. Informatica 31(4):857-880


Macro-averaged F1

Macro-averaged F1 computes the F1-score independently for each class and then takes the unweighted arithmetic mean. It treats all classes equally, regardless of their frequency in the dataset, making it useful for imbalanced multi-class problems.

Powers, D. M. (2011). Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation. Journal of Machine Learning Technologies, 2(1), 37-63.

Sokolova, M., Japkowicz, N., & Szpakowicz, S. (2006). Beyond Accuracy, F-Score and ROC: a Family of Discriminant Measures for Performance Evaluation. AI 2006, 4013, 1015-1021.


MAD Estimation

median absolute deviation
MAD scale estimator
robust scale estimation
Medyan Mutlak Sapma (MAD) Tahmini

Hampel (influence-curve treatment); classical robust statistics

Median Absolute Deviation estimation is a robust measure of statistical dispersion that replaces the standard deviation when outliers are present. Rooted in the influence-curve framework formalised by Hampel (1974), it summarises the spread of a continuous variable using medians instead of means, so a single extreme value cannot distort the result.

Median of absolute deviations from the median (scaled by 1.4826)

Hampel, F. R. (1974). The Influence Curve and Its Role in Robust Estimation. Journal of the American Statistical Association, 69(346), 383-393.

Rousseeuw, P. J. & Croux, C. (1993). Alternatives to the Median Absolute Deviation. Journal of the American Statistical Association, 88(424), 1273-1283.

sn-qn-estimators breakdown-point-analysis robust-mixed-model quantile-regression ols-regression


Madgwick Filter

Madgwick AHRS
gradient descent attitude filter

The Madgwick Filter is a computationally lightweight attitude estimation algorithm that fuses inertial measurements (accelerometer, gyroscope) with magnetic measurements (magnetometer) to compute a quaternion orientation. Introduced by Sebastian Madgwick in 2010, the algorithm uses gradient descent optimization to minimize the error between measured and expected sensor outputs, yielding accurate, drift-free attitude estimates on embedded systems with minimal computational cost. The Madgwick Filter is now ubiquitous in consumer electronics, robotics, and aerospace systems.

Madgwick, S. O. H., Harrison, A. J. L., & Vaidyanathan, R. (2011). Estimation of IMU and MARG orientation using a gradient descent algorithm. IEEE International Conference on Rehabilitation Robotics (ICORR), 1–7.

Madgwick, S. O. H. (2010). An efficient orientation filter for inertial and inertial/magnetic sensor arrays. Report x-io Technologies, University of Bristol, UK.

Sabatini, A. M. (2006). Quaternion-based extended Kalman filter for determining orientation by inertial and magnetic sensing. IEEE Transactions on Biomedical Engineering, 53(7), 1346–1356.


Magnetic Resonance Elastography

MRE
elastography
tissue stiffness mapping

Magnetic Resonance Elastography (MRE) is a non-invasive imaging technique that measures tissue stiffness by encoding the motion of acoustic shear waves into MRI signal and calculating the elastic modulus from wave propagation patterns. Developed by Muthupillai and colleagues in 1995, MRE enables quantitative assessment of tissue mechanics, particularly useful for diagnosing liver fibrosis, cardiac dysfunction, and neurological diseases. It has emerged as a non-invasive alternative to biopsy for staging hepatic fibrosis and is expanding into other organ systems.

MRI-based measurement of tissue stiffness

Muthupillai, R., Lomas, D. J., Rossman, P. J., et al. (1995). Magnetic resonance elastography by direct visualization of propagating acoustic strain waves. Science, 269(5232), 1854-1857.

Huwart, L., Sempoux, C., Vicaut, E., et al. (2008). Magnetic resonance elastography for the noninvasive staging of liver fibrosis. Gastroenterology, 135(1), 32-40.

Kolipaka, A., McGee, K. P., Araoz, P. A., et al. (2008). Magnetic resonance elastography as a method for the assessment of myocardial stiffness: Concepts and applications. Journal of Cardiovascular Magnetic Resonance, 10(1), 43.

dti-tractography ct-iterative-reconstruction quantitative-susceptibility-mapping oct-angiography functional-ultrasound


Magnetisation Transfer Ratio

Magnetisation Transfer Ratio (MTR) is an MRI method that measures the exchange of magnetization between free water protons and protons bound to macromolecules (primarily myelin lipids and proteins). Introduced by Wolff and Balaban in 1989, MTR reflects tissue macromolecular content and is particularly sensitive to myelination, providing a non-invasive estimate of myelin density.

Wolff, S. D., & Balaban, R. S. (1989). Magnetization transfer contrast (MTC) and tissue water proton relaxation in vivo. Journal of Magnetic Resonance Imaging, 10(2), 135–148.

Henkelman, R. M., Huang, X., Lago, A., & Stanisz, G. J. (1993). Quantitative interpretation of magnetization transfer. Magnetic Resonance in Medicine, 29(6), 759–766.


Magnetotellurics

Magnetotellurics (MT) is a passive geophysical method that uses natural variations in Earth's magnetic and electric fields to characterize subsurface electrical conductivity. Developed by Louis Cagniard in 1953, MT measures the impedance relationship between naturally occurring magnetic fluctuations (from solar wind and ionospheric currents) and the resulting electric field, providing information about crustal and upper mantle structures.

Electromagnetic impedance and conductivity imaging

Cagniard, L. (1953). Basic theory of the magnetotelluric method of geophysical prospecting. Geophysics, 18(3), 605-635.

Simpson, F., & Bahr, K. (2005). Practical magnetotellurics. Cambridge University Press.


MAHALANOBIS-DISTANCE

MAHALANOBIS-DISTANCE (Mahalanobis Distance — covariance-adjusted distance accounting for inter-criterion correlations) is a distance multi-criteria decision-making (MCDM) method introduced by Mahalanobis, P. C. in 1936. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Mahalanobis, P. C. (1936). Mahalanobis Distance. Proceedings of the National Institute of Sciences of India


Robust Mahalanobis Distance

Robust Mahalanobis Distance (MCD-based Multivariate Outlier Detection)

MCD Mahalanobis distance
robust mahalanobis
minimum covariance determinant distance
Robust Mahalanobis Uzaklığı

Rousseeuw & Van Zomeren (robust distance); Filzmoser, Garrett & Reimann (multivariate outlier detection)

Robust Mahalanobis Distance flags multivariate outliers by measuring how far each observation lies from the centre of the data using a robust covariance estimate. It builds on the robust-distance framework of Rousseeuw and Van Zomeren (1990) and the multivariate outlier-detection approach of Filzmoser, Garrett and Reimann (2005), replacing the classical mean and covariance with the Minimum Covariance Determinant (MCD) estimate so that the outliers themselves do not distort the distance.

Minimum Covariance Determinant (MCD) location and scatter

Rousseeuw, P. J. & Van Zomeren, B. C. (1990). Unmasking Multivariate Outliers and Leverage Points. Journal of the American Statistical Association, 85(411), 633-639.

Filzmoser, P., Garrett, R. G. & Reimann, C. (2005). Multivariate Outlier Detection in Exploration Geochemistry. Computational Statistics & Data Analysis, 49(2), 561-587.

mad-estimation adjusted-boxplot robust-anova theil-sen-estimator least-trimmed-squares


Mahony Filter

Mahony Complementary Filter for IMU and AHRS

Mahony AHRS
complementary observer attitude filter

The Mahony Filter is a complementary observer-based attitude filter that fuses gyroscope, accelerometer, and magnetometer measurements to estimate quaternion orientation. Developed by Robert Mahony and colleagues in 2008, the filter combines gyroscope rate integration with corrective feedback from vector measurements (accelerometer, compass) using proportional-integral control principles. The Mahony Filter provides similar performance to Kalman Filters but with simpler implementation and lower computational cost, making it ideal for resource-constrained systems and real-time control.

Mahony, R., Hamel, T., & Pflimlin, J. M. (2008). Multirotor aerial vehicles: Modeling, estimation, and control of quadrotors. IEEE Robotics and Automation Magazine, 19(3), 20–32.

Mahony, R., Hamel, T., & Pflimlin, J. M. (2012). Multirotor aerial vehicles: Modeling, estimation, and control of quadrotors. IEEE Robotics & Automation Magazine, 19(3), 20–32.

Valenti, R. G., Dryanovski, I., & Xiao, J. (2016). Keeping a good attitude: A quaternion-based orientation filter for IMUs and MARGs. Sensors, 15(8), 19302–19330.


Maillard Reaction Kinetics

Maillard Reaction Kinetics measures the rate of non-enzymatic browning when amino acids and reducing sugars react under heat. Understanding these kinetics enables optimization of flavor development, control of color changes during processing and storage, and prediction of product quality evolution.

Hodge, J. E. (1953). Chemistry of browning reactions. Journal of Agricultural and Food Chemistry, 1(15), 928-943.


Maintenance Optimization

Optimal Maintenance Policy
Preventive Maintenance Scheduling
Predictive Maintenance Optimization
Bakım Optimizasyonu

Maintenance Optimization is a quantitative framework for determining the timing, type, and frequency of maintenance actions—preventive, predictive, or corrective—that minimize total cost or expected downtime over a system's operational life. Systematic formulations were consolidated by Hongzhou Wang (2002), whose survey unified age-replacement, block-replacement, and imperfect-repair policies under a common cost-rate structure applicable to deteriorating systems across engineering and operations management.

Input: system degradation or failure-rate data

Output: optimal maintenance schedule minimizing cost or downtime

Wang, H. (2002). A survey of maintenance policies of deteriorating systems. European Journal of Operational Research, 139(3), 469–489.


MAIRCA

MAIRCA (Multi-Attributive Ideal-Real Comparative Analysis) is a ranking multi-criteria decision-making (MCDM) method introduced by Pamučar, D., Vasin, Lj., Lukovac, V. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Gap matrix (theoretical vs actual preference)

Pamučar, D., Vasin, Lj., Lukovac, V. (2014). Selection of railway level crossings for investing in security equipment using hybrid DEMATEL-MARICA model. XVI International Scientific-Expert Conference on Railway, Railcon

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


Majority Voting

Majority voting is an ensemble method that combines predictions from multiple base classifiers by selecting the class that receives the most votes. Each base classifier casts one vote for a predicted class, and the final prediction is the class with the majority (plurality). This approach was formalized by Leo Breiman and colleagues in the 1990s as a simple yet effective way to improve classification accuracy.

Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.

Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience.

bagging-ensemble boosting-ensemble stacked-generalization random-forest adaboost


Maki Cointegration Test

Maki Cointegration Test with Multiple Structural Breaks

The Maki cointegration test extends cointegration testing to allow for an unknown number of endogenously-determined structural breaks in the cointegrating relationship. Introduced by Maki (2012), it builds on Gregory and Hansen (1996), enabling detection of cointegration even when relationships shift due to policy changes, institutional reforms, or fundamental regime shifts. This is essential for applied time-series work where structural change is common.

Maki, D. (2012). Tests for cointegration allowing for an unknown number of breaks. Economic Modelling, 29(5), 2011-2015.

Gregory, A. W., & Hansen, B. E. (1996). Residual-based tests for cointegration in models with regime shifts. Journal of Econometrics, 70(1), 99-126.


MALDI-TOF

Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry

MALDI mass spectrometry
MALDI-TOF-MS
laser desorption mass spectrometry

Matrix-Assisted Laser Desorption/Ionization (MALDI) combined with Time-of-Flight (TOF) mass analysis, or MALDI-TOF, is a soft ionization mass spectrometry technique that gently ionizes intact biomolecules and volatile organic compounds, then measures their mass-to-charge ratio by measuring flight time through a field-free drift region. Introduced independently by Karas, Hillenkamp, and Tanaka in 1988, MALDI-TOF revolutionized proteomics, microbiology, and organic analysis by enabling mass determination of proteins and polymers exceeding 100 kDa.

Karas, M., & Hillenkamp, F. (1988). Laser desorption ionization of proteins with molecular masses exceeding 10,000 daltons. Analytical Chemistry, 60(20), 2299-2301.

Tanaka, K., Waki, H., Ido, Y., Akita, S., Yoshida, Y., & Yoshida, T. (1988). Protein and polymer analyses up to m/z 100,000 by laser ionization time-of-flight mass spectrometry. Rapid Communications in Mass Spectrometry, 2(8), 151-153.

Fenn, J. B., Mann, M., Meng, C. K., Wong, S. F., & Whitehouse, C. M. (1989). Electrospray ionization for mass spectrometry of large biomolecules. Science, 246(4926), 64-71.


Male Sexual Health Questionnaire

The MSHQ is a multidimensional self-report questionnaire designed to assess sexual function, problems, and satisfaction in men. Developed by Glina and colleagues and first published in 2008, it measures erectile function, ejaculatory function, and overall sexual satisfaction. The MSHQ exists in multiple versions (full form and brief forms) and has become increasingly used in clinical research, particularly in evaluating sexual outcomes in men with prostate cancer and other urologic conditions.

Glina, S., Natali, A., & the MSHQ Study Group. (2008). The Male Sexual Health Questionnaire: measurement and validation of sexual dysfunction in men. Urology, 72(6), 1319–1326.

Litwin, M. S., & CAP Study Group. (2007). The Cancer of the Prostate Strategic Urologic Research Endeavor database: a new resource for research on prostate cancer. Urology, 70(5S), 34–37.


ML index
Malmquist-Luenberger index
ML productivity

The Malmquist-Luenberger (ML) Productivity Index combines concepts from the Malmquist index and Luenberger's directional distance functions to measure total factor productivity (TFP) change over time. It decomposes productivity growth into technical efficiency change and technological progress, enabling comprehensive productivity assessment without requiring specific functional form assumptions.

Malmquist, S. (1953). Index numbers and indifference surfaces. Trabajos de Estadistica y de Investigacion Operativa, 4(2), 209-242.

Luenberger, D. G. (1992). New optimality conditions for stochastic control problems. Mathematics of Operations Research, 17(3), 657-663.

Chambers, R. G., Chung, Y., & Färe, R. (1996). Benefit and distance functions. Journal of Economic Theory, 70(2), 407-419.


Malmquist Productivity Index

MPI
Malmquist Index
Malmquist DEA Productivity Index
Malmquist Verimlilik Endeksi

The Malmquist Productivity Index (MPI) is a non-parametric measure of total factor productivity (TFP) change over time. Formally grounded in distance functions by Caves, Christensen, and Diewert (1982) and operationalized using Data Envelopment Analysis by Färe, Grosskopf, Norris, and Zhang (1994), MPI decomposes productivity growth into two components: efficiency change (catching-up to the frontier) and technical change (shift of the frontier itself).

Färe, R., Grosskopf, S., Norris, M., & Zhang, Z. (1994). Productivity growth, technical progress, and efficiency change in industrialized countries. American Economic Review, 84(1), 66–83.

Caves, D. W., Christensen, L. R., & Diewert, W. E. (1982). The economic theory of index numbers and the measurement of input, output, and productivity. Econometrica, 50(6), 1393–1414.


Malnutrition Screening Tool

MST
Malnutrition Screening
Nutritional Risk Screen

The Malnutrition Screening Tool (MST), developed by Michelle Ferguson and colleagues in 1999, is a brief, validated screening instrument designed to identify hospitalized patients at risk for malnutrition. The tool consists of two simple questions about recent unintentional weight loss and reduced food intake, yielding a quick numerical score. Since its publication, the MST has become widely adopted in acute hospitals, residential aged care facilities, and community settings as a rapid, reliable first-line screen for nutritional risk.

Ferguson, M., Capra, S., Bauer, J., & Banks, M. (1999). Development of a valid and reliable malnutrition screening tool for adult acute hospital patients. Nutrition, 15(6), 458-464.

Stratton, R. J., Hackston, A., Longmore, D., Dixon, R., Price, S., Stroud, M., King, B., & Elia, M. (2004). Malnutrition in hospital outpatients and inpatients: prevalence, concurrent validity and ease of use of the 'Malnutrition Screening Tool' (MST) for adults. Br J Nutr, 92(5), 799-808.


Mamba (State Space Model)

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Mamba
State space models
Selective state space

Deep Learning, Sequence Models, State Space Models

Mamba is a sequence model architecture introduced by Gu and Dao in 2023 that achieves linear-time complexity while maintaining strong performance on language modeling tasks. By combining state space models with input-dependent selectivity, Mamba addresses the quadratic complexity of transformers while preserving modeling power.

Gu, A., & Dao, C. (2023). Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.08956.

vision-transformer vision-mamba latent-diffusion-models masked-autoencoders


MANCOVA

MANCOVA — Çok Değişkenli Kovaryans Analizi

Extension of MANOVA and ANCOVA traditions; consolidated in multivariate textbooks by the 1970s–1980s

MANCOVA (Multivariate Analysis of Covariance) is a parametric hypothesis test that simultaneously compares two or more groups on multiple continuous dependent variables while statistically controlling for one or more covariates. It extends MANOVA by incorporating covariate adjustment, a tradition consolidated in multivariate statistical methodology by the 1970s and authoritatively documented by Tabachnick and Fidell (2019).

Parametric multivariate mean comparison with covariate control

Wilks' Lambda (and other multivariate F approximations)

Box's M for variance-covariance homogeneity

Tabachnick, B. G. & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson.

manova ancova one-way-anova hotelling-t2 discriminant-analysis


Young Mania Rating Scale

The YMRS is an 11-item clinician-administered rating scale designed to assess the severity of manic and hypomanic symptoms in bipolar disorder. Developed by Young and colleagues in 1978, it is the gold standard outcome measure in bipolar disorder research and the primary efficacy endpoint in mood stabilizer and antipsychotic trials for acute mania. The YMRS captures core mania features (elevated mood, increased goal-directed activity, racing thoughts, reduced need for sleep, increased talkativeness, distractibility, and irritability) and is sensitive to both pharmacological and psychotherapeutic interventions.

Young, R. C., Biggs, J. T., Ziegler, V. E., & Meyer, D. A. (1978). A rating scale for mania: Reliability, validity and sensitivity. British Journal of Psychiatry, 133(5), 429–435.

Altman, E. G., Hedeker, D., Peterson, J. L., & Davis, J. M. (1994). The Altman Self-Rating Mania Scale. Biological Psychiatry, 42(12), 948–955.

Muralidharan, K., & Koshy, G. (2005). A review of the Mania Rating Scale instruments and their role in bipolar disorder. Indian Journal of Psychiatry, 47(1), 9–15.


Mann-Whitney U test

Mann-Whitney-Wilcoxon test
Wilcoxon rank-sum test
Mann-Whitney U Testi

The Mann-Whitney U test is the nonparametric alternative to the independent samples t-test, comparing two independent groups by ranking all observations together rather than relying on their means. It was introduced by H. B. Mann and D. R. Whitney in 1947 and does not require the data to be normally distributed.

Mann-Whitney U (normal approximation for large samples)

Mann, H. B. & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. Annals of Mathematical Statistics, 18(1), 50–60.

Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed.). SAGE.

independent-t-test wilcoxon-signed-rank kruskal-wallis permutation-test


MANOVA

Multivariate ANOVA
Çok Değişkenli ANOVA (MANOVA)

Samuel Stanley Wilks (Wilks' Lambda, 1932); Roy, Hotelling, Pillai (mid-20th c.)

MANOVA is a parametric hypothesis test that simultaneously compares group means across multiple continuous dependent variables, controlling the inflation of Type I error that would result from running separate ANOVAs. Key multivariate test statistics — Wilks' Lambda, Pillai's Trace, Hotelling-Lawley Trace, and Roy's Greatest Root — were developed between the 1930s and 1950s, with Wilks' Lambda formalised by Samuel Stanley Wilks in 1932.

Wilks' Lambda / Pillai's Trace / Hotelling-Lawley Trace / Roy's Greatest Root

Tabachnick, B.G. & Fidell, L.S. (2013). Using Multivariate Statistics (6th ed.). Pearson.

one-way-anova repeated-measures-anova discriminant-analysis independent-t-test ancova


Manual Muscle Testing

Lucille Daniels and Catharine Worthingham

Manual muscle testing (MMT) is a clinical examination technique that quantifies muscle strength by applying manual resistance to isometric contractions and grading the result on a standardized scale (typically 0-5). Developed by Daniels and Worthingham in the 1940s, MMT remains the primary bedside method for assessing muscle weakness in neuromuscular and neurological disorders, establishing rehabilitation baselines, and monitoring treatment effectiveness.

Kendall, F. P., McCreary, E. K., Provance, P. G., Rodgers, M. M., & Romani, W. A. (2005). Muscles: Testing and function with posture and pain (5th ed.). Lippincott Williams & Wilkins.

Daniels, L., & Worthingham, C. (1972). Muscle testing: Techniques of manual examination (4th ed.). W.B. Saunders Company.


Map Algebra

Cartographic Modeling
Raster Algebra
Grid Algebra
Harita Cebiri

Map Algebra is a rule-based language and computational framework for deriving new raster layers from existing ones by applying arithmetic, logical, or statistical operations cell by cell or across neighborhoods. Formalized by Dana Tomlin in 1990, it is the foundational algebraic system underlying raster GIS analysis and is widely used in environmental science, urban planning, hydrology, and land-use modeling whenever spatially explicit calculations on gridded data are required.

Raster (grid) layers in common extent and resolution

Output: Derived raster layer(s)

Tomlin, C. D. (1990). Geographic Information Systems and Cartographic Modeling. Prentice Hall.


Mapper Algorithm

Mapper Algorithm for Topological Data Analysis

Topological Mapper
TDA Mapper
Reeb Graph Approximation
Eşleyici Algoritma

The Mapper algorithm is a method in topological data analysis (TDA) that produces a graph-based summary of the shape of high-dimensional point cloud data. Introduced by Singh, Mémoli, and Carlsson in 2007 at the Eurographics Symposium on Point-Based Graphics, Mapper constructs a simplicial complex — typically a graph — that captures the global topological and geometric structure of a dataset without requiring a fixed embedding or metric assumption.

Output: Simplicial complex (graph)

Depends on cover resolution and clustering choice

Singh, G., Mémoli, F., & Carlsson, G. (2007). Topological methods for the analysis of high dimensional data sets and 3D object recognition. Eurographics Symposium on Point-Based Graphics, 91–100.


Mapping Review

evidence map
systematic map
research map
literature map

Late 1990s–2000s; major methodological formalization ~2010s

Buckland & Gann (1998); formalized by systematic review community (Campbell Collaboration, Collaboration for Environmental Evidence)

A mapping review (also called a systematic map or evidence map) is a form of systematic review that aims to chart the extent, range, and nature of evidence on a broad topic rather than synthesize findings into a single pooled answer. It categorizes studies by key dimensions — such as intervention type, population, outcome, and study design — and presents the resulting landscape visually and tabularly so that researchers and practitioners can identify clusters of evidence, knowledge gaps, and priorities for future primary research or deeper synthesis.

Bibliographic records, abstracts, full-text papers

James, K. L., Randall, N. P., & Haddaway, N. R. (2016). A methodology for systematic mapping in environmental sciences. Environmental Evidence, 5(1), 7.

Miake-Lye, I. M., Hempel, S., Shanman, R., & Shekelle, P. G. (2016). What is an evidence map? A systematic review of published evidence maps and their definitions, methods, and products. Systematic Reviews, 5(1), 28.

scoping-review systematic-literature-review bibliometric-analysis narrative-review umbrella-review co-word-analysis


MARA

MARA (Magnitude of the Area for the Ranking of Alternatives) is a ranking multi-criteria decision-making (MCDM) method introduced by Gligorić, M., Gligorić, Z., Lutovac, S., Negovanović, M., Lugijevskij, R. in 2022. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Gligorić, M., Gligorić, Z., Lutovac, S., Negovanović, M., Lugijevskij, R. (2022). Novel hybrid MPSI-MARA decision-making model for support system selection in an underground mine. Systems


MARCOS

MARCOS (Measurement of Alternatives and Ranking according to Compromise Solution) is a ranking multi-criteria decision-making (MCDM) method introduced by Stević, Ž., Pamučar, D., Puška, A., Chatterjee, P. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Utility function (ideal + anti-ideal reference)

Stević, Ž., Pamučar, D., Puška, A., Chatterjee, P. (2020). Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS). Computers & Industrial Engineering


Marginal structural model in education research

Marginal Structural Model for Causal Inference in Education Research

MSM
marginal structural model
MSM with inverse probability weighting
IPW-MSM

2000 (method); 2006 (canonical education application)

James M. Robins, Miguel A. Hernán, Babette Brumback (epidemiology); Guanglei Hong & Stephen Raudenbush (education application)

A marginal structural model (MSM) is a causal inference technique that uses inverse probability weighting to estimate the effect of a treatment or educational intervention that changes over time. Introduced by Robins, Hernán and Brumback (2000) in epidemiology and brought into education by Hong and Raudenbush (2006), MSMs handle time-varying confounding — a challenge that conventional regression cannot resolve.

Causal inference / weighted regression model

Longitudinal or panel data with time-varying treatments

Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.

Hong, G., & Raudenbush, S. W. (2006). Evaluating kindergarten retention policy: A case study of causal inference for multilevel observational data. Journal of the American Statistical Association, 101(475), 901-910.

inverse-probability-weighting propensity-score-matching difference-in-differences instrumental-variables causal-mediation-analysis regression-discontinuity


Marginal Structural Model

Marginal Structural Model with Inverse Probability of Treatment Weighting

MSM
MSM-IPTW
marginal structural Cox model
weighted structural model

James M. Robins, Miguel A. Hernan, Babette Brumback

A marginal structural model is a causal modeling framework designed to estimate the effect of a time-varying treatment in the presence of time-varying confounders that are themselves affected by prior treatment. By reweighting observations with inverse probability of treatment weights, MSMs create a pseudo-population in which confounding is eliminated, enabling unbiased estimation of causal treatment contrasts even when standard regression adjustments would fail.

Longitudinal / time-varying treatment and covariate data

Hernan, M. A., & Robins, J. M. (2020). Causal Inference: What If. Chapman & Hall/CRC.

inverse-probability-weighting propensity-score-weighting doubly-robust-estimation difference-in-differences g-computation time-varying-confounding


ENRICH Marital Satisfaction Scale

EMS
ENRICH Satisfaction Scale
ENRICH Couple Assessment

The ENRICH (Enriching Relationships: Issues, Communication, Happiness) program is a comprehensive couple assessment and enrichment system developed by David Olson that includes multiple relationship assessment tools. The ENRICH Marital Satisfaction Scale is a subset of the full ENRICH Couple Inventory and measures couple satisfaction across key relationship domains. Widely used in premarital counseling, relationship enrichment programs, and couple therapy, the ENRICH approach assesses both relationship strengths and areas for improvement, positioning it as both diagnostic and developmentally focused.

Olson, D. H. (2011). ENRICH couple inventory: Couple communication, compatibility, and satisfaction assessment. Minneapolis: Life Innovations.

Olson, D. H., & Olson, A. K. (2008). The couple checkup: Assessing relationship health. In H. A. Liddle, C. L. Rowe, G. M. Dakof, & D. A. Henderson (Eds.), Family therapy for adolescent substance abuse (pp. 25-48). New York: Guilford Press.


Markerless Motion Capture

Marker-free tracking
Vision-based motion capture
Deep learning pose estimation

Markerless motion capture infers the 3D positions and joint angles of a moving subject from video sequences using computer vision and machine learning. Pioneered by deep learning approaches such as OpenPose and MediaPipe, it eliminates the need for reflective markers or inertial sensors, making motion capture accessible and practical for real-world applications.

Cao, Z., Simon, T., Wei, S. E., & Sheikh, Y. (2017). Realtime multi-person 2D pose estimation using part affinity fields. IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.


MARKOR Market Orientation Scale

Market Orientation Measurement
Kohli-Jaworski Scale

Ajay K. Kohli, Bernard J. Jaworski, Ajith Kumar

The MARKOR scale, developed by Kohli, Jaworski, and Kumar (1993), measures organizational market orientation—the degree to which an organization actively gathers and uses market intelligence to guide strategy and decision-making. MARKOR captures three core dimensions: Intelligence Generation (collecting customer and competitor information), Dissemination (sharing market knowledge across departments), and Responsiveness (acting on market insights). The scale is widely used in strategic management research to diagnose whether organizations are truly customer-focused or operate in a more internally driven manner.

Multi-dimensional organizational market orientation scale

Kohli, A. K., Jaworski, B. J., & Kumar, A. (1993). MARKOR: A Measure of Market Orientation. Journal of Marketing Research, 30(4), 467-477.

Jaworski, B. J., & Kohli, A. K. (1993). Market Orientation: Antecedents and Consequences. Journal of Marketing, 57(3), 53-70.


Market Segmentation Analysis

Customer Segmentation
Market Partitioning

Market Segmentation Analysis is a systematic approach to dividing a heterogeneous market into smaller, homogeneous groups (segments) that share similar needs, behaviors, preferences, or characteristics. Developed through advances in statistical clustering and customer analytics, this methodology enables companies to tailor marketing strategies, product offerings, and customer experiences to specific audience groups rather than treating the market as a single entity.

Wedel, M., & Kamakura, W. A. (2002). Introduction to the Special Issue on Market Segmentation. International Journal of Research in Marketing, 19(3), 181-183.

Kotler, P. (1997). Marketing Management: Analysis, Planning, Implementation and Control (9th ed.). Prentice Hall.

Dolnicar, S., Grün, B., & Leisch, F. (2018). Market Segmentation Analysis: Understanding It, Doing It, and Making It Useful. Springer.

brand-equity-measurement customer-lifetime-value customer-journey-mapping willingness-to-pay-estimation advertising-effectiveness-study


Market Sensing Capability Scale

Market Sensing Capability (MSC) Measurement Scale

Ajay Kohli, Bernard Jaworski, and George S. Day

Market Sensing Capability (MSC) refers to an organization's ability to systematically gather, interpret, and respond to market information about customers, competitors, and market trends. Building on Kohli and Jaworski's (1990) market orientation construct and George Day's (1994) framework of market-driven organizations, the MSC scale measures three interconnected processes: intelligence generation (acquiring market information), dissemination (sharing information across functions), and responsiveness (acting on market insights). Organizations with strong MSC detect competitive threats earlier, understand customer needs more deeply, and adapt strategies faster than competitors with weaker sensing capabilities.

Kohli, A. K., & Jaworski, B. J. (1990). Market orientation: The construct, research propositions, and managerial implications. Journal of Marketing, 54(2), 1–18.

Day, G. S. (1994). The capabilities of market-driven organizations. Journal of Marketing, 58(4), 37–52.

Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28(13), 1319–1350.

dynamic-capabilities-scale entrepreneurial-orientation-scale market-sensing-capability-scale innovation-ambidexterity-scale strategic-orientation-scale


Marketing Mix Modeling

MMM
Econometric Modeling
Attribution Modeling

David Hanssens, Leonard Parsons, and Randall Schultz

Marketing Mix Modeling (MMM) is an econometric methodology for estimating the impact of various marketing activities (advertising, pricing, promotions, distribution) on sales or other business outcomes. Developed through work by Hanssens, Parsons, and Schultz, MMM integrates time-series data on marketing spend, sales, and market factors to quantify the return on investment for each marketing channel and inform budget allocation decisions.

Hanssens, D. M., Parsons, L. J., & Schultz, R. L. (2001). Market Response Models: Econometric and Time Series Analyses (2nd ed.). Kluwer Academic Publishers.

Naik, P. A., Raman, K., & Winer, R. S. (2005). Planning Marketing-Mix Strategies in the Presence of Interaction Effects. Marketing Science, 24(1), 25-34.

Madigan, D. (2012). Bayesian Methods for Complex Data. Annual Review of Statistics and Its Applications, 1(1), 1-30.

advertising-effectiveness-study customer-lifetime-value willingness-to-pay-estimation brand-equity-measurement market-segmentation-analysis