Equivalence / Non-Inferiority Trial

Equivalence and Non-Inferiority Clinical Trial Design

Denklik ve Üstünlük Olmayan Çalışma (Equivalence / Non-Inferiority)

Schuirmann, D.J. / EMA regulatory framework

An equivalence or non-inferiority trial is a clinical study design that tests whether a new intervention is clinically equivalent to, or no worse than, an established standard by a pre-specified margin. Codified in Schuirmann's 1987 Two One-Sided Tests (TOST) framework and embedded in EMA and FDA regulatory guidance, this design is the regulatory standard for generic drug approval and medical device testing.

Parametric equivalence / non-inferiority test

TOST (Two One-Sided Tests) for equivalence; one-sided confidence interval for non-inferiority

Generic drug approval (FDA, EMA), medical device testing

Schuirmann, D.J. (1987). A Comparison of the Two One-Sided Tests Procedure and the Power Approach. Journal of Pharmacokinetics and Biopharmaceutics, 15(6), 657–680.

EMA (2010). Guideline on the Investigation of Bioequivalence. CPMP/EWP/QWP/1401/98 Rev. 1. European Medicines Agency.

equivalence-test-tost randomized-controlled-trial crossover-design sequential-analysis adaptive-design power-analysis-ttest


Equivalent Static Analysis

Equivalent Static Analysis for Seismic Design

Lateral force method
Simplified seismic design
Static equivalent method

SEAOC (Structural Engineers Association of California)

Equivalent static analysis is the simplest seismic design method, representing earthquake effects as a single static lateral force applied at the center of mass or distributed over the building height. Standardized by SEAOC in 1959 and incorporated into modern building codes, it is the most commonly used method for designing regular buildings.

Simplified linear method for earthquake-equivalent lateral forces

SEAOC (1959). Lateral Forces by Virtual Work. Proceedings of the Structural Engineers Association of California, 28(1), 1-16.

ASCE/SEI (2016). Minimum Design Loads and Associated Criteria for Buildings and Other Structures (ASCE/SEI 7-16). American Society of Civil Engineers.

Chopra, A. K. (2017). Dynamics of Structures: Theory and Applications to Earthquake Engineering (5th ed.). Pearson.


Erlang C Model

M/M/c Queue
Multi-Server Queueing Model
Erlang Delay Formula
Erlang-C Bekleme Modeli

The Erlang C model is a steady-state queueing formula that determines the probability a customer must wait before being served in a system with c parallel servers, Poisson arrivals at rate lambda, and exponentially distributed service times. Originally developed by Danish engineer Agner Krarup Erlang in the early twentieth century for telephone exchange design, and formalized in the queueing theory literature by Cooper (1981), it remains the canonical staffing model for call centers and service operations worldwide.

Poisson arrivals, exponential service times

Output: Probability of waiting, expected wait time, required server count

Cooper, R. B. (1981). Introduction to Queueing Theory (2nd ed.). North-Holland.


ERS Point-Optimal Test

Elliott-Rothenberg-Stock Point-Optimal Unit-Root Test

ERS P-test
Point-Optimal Unit-Root Test
ERS PT statistic
ERS Nokta-Optimal Birim Kök Testi

The Elliott-Rothenberg-Stock (ERS) Point-Optimal test, introduced in their landmark 1996 Econometrica paper, is a near-efficient parametric procedure for testing whether a univariate time series contains a unit root. By first applying GLS detrending at a carefully chosen local-to-unity value and then computing a likelihood-ratio-type statistic, it achieves power close to the Gaussian power envelope—making it one of the most powerful unit-root tests available to applied econometricians.

Near-efficient against local-to-unity alternatives

Elliott, G., Rothenberg, T. J., & Stock, J. H. (1996). Efficient tests for an autoregressive unit root. Econometrica, 64(4), 813–836.


ERVD

ERVD (Election based on Relative Value Distances) is a ranking multi-criteria decision-making (MCDM) method introduced by Shyur, H. J., Yin, L., Shih, H. S., Cheng, C. B. in 2015. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Prospect-theory value function with reference-point separation

Shyur, H. J., Yin, L., Shih, H. S., Cheng, C. B. (2015). A multiple criteria decision making method based on relative value distances. Foundations of Computing and Decision Sciences


Emerging Sources Citation Index

Web of Science Emerging Sources Citation Index

The Emerging Sources Citation Index (ESCI) is a supplement to Web of Science Core Collection launched by Clarivate Analytics in 2015 to expand journal coverage beyond the traditional Science Citation Index Expanded (SCI-E), Social Sciences Citation Index (SSCI), and Arts & Humanities Citation Index (A&HCI). ESCI includes high-quality, peer-reviewed journals that are newer, from underrepresented geographic regions, or from emerging disciplines not yet established in traditional indexes. Unlike SCI-E/SSCI journals, ESCI journals do not initially receive Impact Factor calculations. However, ESCI serves as a pathway: journals demonstrating sustained citation impact and adherence to quality standards may be promoted to SCI-E/SSCI and gain Impact Factor recognition. ESCI addresses systematic geographic and disciplinary biases in traditional indexing by including quality journals from developing economies, Eastern Europe, Latin America, and Asia.

Clarivate Analytics. (2024). Emerging Sources Citation Index. Retrieved from https://clarivate.com/webofsciencegroup/solutions/web-of-science-core-collection/

Clarivate. (2019). Web of Science Expands with Emerging Sources Citation Index. https://clarivate.com/webofsciencegroup/blog/

web-of-science scopus-database doaj-directory ulrichsweb journal-citation-reports


Estrus Detection

Estrus Detection and Reproductive Status Monitoring

heat detection
estrous cycle monitoring
sexual receptivity assessment

Estrus detection is the identification of the fertile period in female livestock, when ovulation is imminent and animals are sexually receptive. Formalized by reproductive physiologists in the 1960s-1970s, the practice combines behavioral observation, physical signs, and technology-enabled monitoring to identify the optimal timing for breeding. Accurate estrus detection is fundamental to reproductive efficiency, conception rates, and profitability in livestock operations.

De Vries, A., Steevens, B., & Kristensen, A. R. (2013). Accelerated improvement of dairy herd reproductive performance: Estrus detection and breeding timing revisited. Journal of Dairy Science, 96(2), 1-15.

Nebel, R. L., Jobst, S. M., Mullin, P. H., Stanisiewski, E. P., & Lednor, A. J. (1997). Evaluation of systematic breeding programs for dairy cattle. I. Reproductive performance. Journal of Dairy Science, 80(5), 910-920.

Hunt, V. M., Frick, M. A., & Rutten, C. J. (2015). Sensors in dairy production. Journal of Dairy Science, 98(7), 4625-4639.


Ethical Leadership Scale

The Ethical Leadership Scale (ELS) is a 10-item instrument measuring the degree to which leaders model ethical behavior and hold followers accountable to ethical standards. Developed by Brown, Treviño, and Harrison in 2005, the ELS operationalizes ethical leadership, assessing leader conduct and norm-setting that shape organizational ethics.

Brown, M. E., Treviño, L. K., & Harrison, D. A. (2005). Ethical leadership: A social learning perspective for construct development and testing. Organizational Behavior and Human Decision Processes, 97(2), 117-134.

Yukl, G. A. (2002). Leadership in organizations (5th ed.). Prentice Hall.

authentic-leadership-scale organizational-trust-scale corporate-social-responsibility-scale toxic-leadership-scale


Ethics Committee Application Process

Preparing and Submitting a Research Protocol for Ethics Review

IRB application
REC application
ethics approval
protocol submission

U.S. Department of Health and Human Services; International research oversight organizations

Submitting a research protocol to an ethics committee (IRB, REC, or equivalent) is a mandatory procedural gateway in human subjects research. The application process requires researchers to document their study design, justify scientific rationale, disclose risks and benefits, provide participant protections (informed consent forms), and address ethical considerations. The submission includes a completed ethics application form, protocol document, consent forms, researcher CVs, and evidence of institutional support. This standardized process enables ethics committees to conduct rigorous, timely, and consistent review before research commences.

U.S. Department of Health and Human Services. (2018). Protection of Human Subjects. Code of Federal Regulations Title 45, Part 46, Section 46.109.

International Council for Harmonisation. (2016). ICH Harmonised Guideline: Integrated Addendum to ICH E6(R1). Good Clinical Practice E6(R2).

Health Research Authority. (2021). Guidance for Applicants: Applying for Ethics Review. UK Research Ethics Service.

U.S. Department of Health and Human Services, Office for Human Research Protections. (2019). Informed Consent FAQs. National Institutes of Health.

ethics-committee-types risk-benefit-assessment vulnerable-populations-research clinical-trial-registration waiver-of-informed-consent


Types of Ethics Committees in Research

Classification and Structure of Research Ethics Review Bodies

IRB
REC
Ethics Committee
Institutional Review Board
Research Ethics Committee

Research ethics committees are independent governance bodies established to review and oversee human subjects research. In the United States, these are called Institutional Review Boards (IRBs); in the United Kingdom and Commonwealth nations, Research Ethics Committees (RECs); and in European Union and other jurisdictions, they are termed Ethics Committees. These bodies operate under national regulations—45 CFR 46 in the U.S., the Medicines for Human Use (Clinical Trials) Regulations in the UK, and the EU Clinical Trials Regulation in Europe—to ensure research protects participant rights, safety, and welfare.

U.S. Department of Health and Human Services. (2018). Protection of Human Subjects. Code of Federal Regulations Title 45, Part 46.

U.S. Food and Drug Administration. (2013). Institutional Review Boards: Frequently Asked Questions. Center for Drug Evaluation and Research.

ethics-committee-application waiver-of-informed-consent vulnerable-populations-research risk-benefit-assessment clinical-trial-registration


Multigroup Ethnic Identity Measure

The Multigroup Ethnic Identity Measure (MEIM) is a self-report instrument designed to assess ethnic identity development among adolescents and young adults from diverse ethnic and cultural backgrounds. Originally developed by Phinney in 1992, the MEIM measures two primary dimensions: ethnic identity search (active exploration of one's ethnicity) and affirmation-belonging-commitment (positive feelings and sense of belonging to one's ethnic group). The instrument is widely used in developmental, clinical, and health research to evaluate ethnic identity formation, its relationship to psychological well-being, and health outcomes across diverse populations.

Phinney, J. S. (1992). The Multigroup Ethnic Identity Measure: A new scale for use with adolescents and young adults from diverse groups. Journal of Adolescent Research, 7(2), 156–176.

Roberts, R. E., Phinney, J. S., Masse, L. C., Chen, Y. R., Roberts, C. R., & Romero, A. (1999). The structure of ethnic identity of young adolescents from diverse ethnocultural groups. Journal of Early Adolescence, 19(3), 301–322.

social-distance-scale racism-and-life-experiences-scale acculturative-stress-scale cultural-competence-assessment


Ethnographic Research

Ethnography
Participatory Observation
Field Research

Anthropology (Malinowski, Boas); applied in health and sociology (Geertz)

Ethnographic research is an immersive qualitative methodology in which researchers spend prolonged time in a community, organization, or social setting, combining participant observation, interviews, and document analysis to develop a rich, contextual understanding of a group's beliefs, practices, and social structures. Grounded in anthropology and refined for health, organizational, and social research, ethnography produces 'thick description' (Geertz 1973) that reveals the meaning and context underlying observable behavior.

Geertz, C. (1973). The interpretation of cultures: Selected essays. Basic Books.

Hammersley, M., & Atkinson, P. (2006). Ethnography: Principles in practice (3rd ed.). Routledge.

Spradley, J. P. (1980). Participant observation. Holt, Rinehart and Winston.

participant-observation field-notes thick-description case-study-research action-research


Ethnography

Etnografi
participant observation
fieldwork
ethnographic research

c. 1922 (Malinowski's Argonauts of the Western Pacific)

Bronisław Malinowski (modern ethnography); rooted in 19th-century anthropology

Ethnography is a qualitative research tradition in which a researcher immerses themselves in a social group or community over an extended period — typically three to six months or longer — to study its culture, values, and behaviours in their natural setting. Originating in social and cultural anthropology, and consolidated as a rigorous method by Bronisław Malinowski in the early twentieth century, ethnography produces rich, contextualised accounts of how people live, work, and make meaning together.

Field notes, interviews, documents, artefacts

Participant observer embedded in the setting

Output: Thick descriptive account of culture, values, and behaviour

Hammersley, M. & Atkinson, P. (2019). Ethnography: Principles in Practice (4th ed.). Routledge.

grounded-theory case-study focus-group phenomenology narrative-inquiry


ETL Process

Ralph Kimball and data warehouse pioneers

The Extract-Transform-Load (ETL) process is a systematic approach to moving data from source systems into a target repository. Formalized in the context of data warehousing, ETL pipelines extract data from diverse operational sources, apply business rules and data quality checks, and load the results into data warehouses and analytical systems.

Kimball, R. (1996). The Data Warehouse Toolkit: Practical Techniques for Building Dimensional Data Warehouses. New York: John Wiley & Sons.

Kakish, K., & Kraft, T. (2012). The six pillars of enterprise data quality. DM Direct, 5(6), 44-47.

Vassiliadis, P., Quix, C., Vassalos, V., & Jarke, M. (2000). Data warehouse process management. Information Systems, 25(2), 111-125.

data-warehousing data-quality change-data-capture data-validation data-reconciliation


ETS Model

Error, Trend, Seasonal (ETS) Exponential Smoothing

ETS — Hata/Trend/Mevsimsellik Üstel Düzleştirme

Hyndman, Koehler, Ord & Snyder (state space framework)

ETS is a comprehensive exponential smoothing framework that automatically selects additive or multiplicative combinations of the error (E), trend (T) and seasonal (S) components of a time series. Formalised as an innovations state space model by Hyndman, Koehler, Ord and Snyder in 2008, it unifies and generalises the Holt-Winters family of forecasting methods.

Hyndman, R. J., Koehler, A. B., Ord, J. K. & Snyder, R. D. (2008). Forecasting with Exponential Smoothing: The State Space Approach. Springer.

Hyndman, R. J. & Athanasopoulos, G. (2021). Forecasting: Principles and Practice (3rd ed.). OTexts.

holt-winters simple-exponential-smoothing state-space-model structural-time-series arima


ETSformer

ETSformer (Exponential Smoothing Transformer)

Exponential Smoothing Transformer
ETS Transformer
ETSformer forecasting model
Üstel Düzleştirme Transformatörü

ETSformer is a deep learning architecture for time-series forecasting introduced by Woo et al. in 2022. It integrates classical exponential smoothing principles directly into the Transformer framework by replacing standard self-attention with an exponential smoothing attention mechanism. The model decomposes a time series into level, growth (trend), and seasonal components, allowing it to leverage both the long-range dependency modeling of Transformers and the interpretable structure of statistical ETS models.

Hybrid decomposition-based Transformer architecture

Input: Univariate or multivariate time series

Woo, G., Liu, C., Sahoo, D., Kumar, A., & Hoi, S. (2022). ETSformer: Exponential smoothing transformers for time-series forecasting. arXiv preprint.


Eulerian-Lagrangian Model

ELM
two-fluid model
multiphase Eulerian-Lagrangian

The Eulerian-Lagrangian Model (ELM) is a framework for simulating multiphase flows by treating the continuous phase (liquid or gas) using Eulerian descriptions (fixed grid) and discrete dispersed phases (particles, droplets, bubbles) using Lagrangian tracking. Developed by Crowe and collaborators in 1977, this approach exploits the strengths of both perspectives: Eulerian methods for the bulk continuous phase and Lagrangian methods for individual dispersed elements. ELM is widely used in industrial applications including spray combustion, pneumatic conveying, and particle-laden flows.

Crowe, C., Sommerfeld, M., & Tsuji, Y. (2011). Multiphase Flows with Droplets and Particles (2nd ed.). CRC Press.

Elghobashi, S. (1994). On predicting particles-laden turbulent flows. Applied Scientific Research, 52(4), 309-329.

Sanders, R. S., & Loeffler, A. L. (1998). Modeling the effects of bubble interactions on the viscosity of bubbly flows. International Journal of Multiphase Flow, 24(3), 345-357.

reynolds-averaged-navier-stokes large-eddy-simulation volume-of-fluid lattice-boltzmann-method direct-numerical-simulation


Evaluation-Focused Case-Focused Mixed Methods Design

mixed methods program evaluation case design

Jennifer C. Greene; John Creswell and Vicki Plano Clark (mixed methods evaluation synthesis)

Evaluation-focused case-focused mixed methods integrates an explicit program evaluation framework with in-depth case study inquiry, combining qualitative and quantitative data within a bounded unit — a program, site, or organization — to render both descriptive understanding and evaluative judgments about merit, worth, or significance. The design serves applied evaluation contexts where holistic case understanding is needed alongside evidence-based performance conclusions.

Qualitative (interviews, documents, observations) and quantitative (surveys, administrative records) within a bounded case

Greene, J. C. (2007). Mixed Methods in Social Inquiry. Jossey-Bass.

Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.


Evaluation-Focused Concurrent Embedded Mixed Methods Design

concurrent embedded evaluation design
embedded mixed methods evaluation
nested concurrent evaluation design
mixed methods program evaluation

1989–2007 (Greene et al. 1989 for mixed evaluation; Creswell & Plano Clark 2007 for embedded design typology)

Jennifer C. Greene; John W. Creswell & Vicki L. Plano Clark

Evaluation-focused concurrent embedded mixed methods is a research design in which both quantitative and qualitative data are collected simultaneously within a program evaluation context, with one strand nested inside and playing a supporting role to the dominant strand. The design produces outcome evidence alongside embedded process or contextual evidence from the same evaluation cycle, without extending the timeline.

Simultaneous quantitative (primary) and qualitative (embedded) data, or vice versa

concurrent-triangulation-design explanatory-sequential-mixed-methods exploratory-sequential-mixed-methods convergent-parallel-mixed-methods program-evaluation embedded-case-study


Evaluation-focused Explanatory Sequential Mixed Methods Design

explanatory sequential evaluation design

sequential explanatory mixed-methods evaluation

2003 (explanatory sequential core); evaluation adaptation widely documented by 2010s

John W. Creswell & Vicki L. Plano Clark (explanatory sequential core); adapted for evaluation contexts by the program evaluation community

Evaluation-focused explanatory sequential mixed methods is a two-phase research design in which a quantitative evaluation phase — typically measuring program outcomes, treatment effects, or performance indicators — is conducted first and then followed by a qualitative phase specifically designed to explain, contextualise, or interpret the quantitative findings. The design is widely used in program evaluation, policy research, and educational assessment where numbers reveal what happened but qualitative data reveal why.

Quantitative outcome data followed by qualitative interview/focus-group data

Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). SAGE Publications.

Plano Clark, V. L., & Ivankova, N. V. (2016). Mixed Methods Research: A Guide to the Field. SAGE Publications.

explanatory-sequential-mixed-methods exploratory-sequential-mixed-methods convergent-parallel-mixed-methods program-evaluation embedded-mixed-methods transformative-mixed-methods


Evaluation-focused exploratory sequential mixed methods

Evaluation-Focused Exploratory Sequential Mixed Methods Design

exploratory sequential evaluation design

John W. Creswell & Vicki L. Plano Clark (exploratory sequential base); Jennifer C. Greene & Donna M. Mertens (evaluation framing)

The evaluation-focused exploratory sequential mixed methods design combines program evaluation goals with a two-phase sequential structure: qualitative inquiry precedes and informs a quantitative phase. Phase 1 explores stakeholder experiences or program processes through interviews or focus groups; the findings build an instrument or framework used to measure outcomes quantitatively in Phase 2. The approach is used when little is known about a program's mechanisms, and both understanding and generalizing findings to a wider population matter.

Qualitative data (phase 1) then quantitative data (phase 2)

Mertens, D. M. (2009). Transformative Research and Evaluation. Guilford Press.


Evaluation-focused Intervention Mixed Methods Design

intervention mixed methods evaluation
mixed methods intervention evaluation
program evaluation mixed methods
evaluation mixed methods

Creswell & Plano Clark (systematized); roots in evaluation research by Patton and Shadish

Evaluation-focused intervention mixed methods is a research design that embeds both quantitative and qualitative strands within an intervention or program evaluation study. It combines outcome measurement — typically from a randomized or quasi-experimental trial — with qualitative investigation of how and why the intervention worked, for whom, and under what conditions. The design is widely used in health, education, social service, and policy evaluation contexts where understanding mechanisms and context is as important as measuring effectiveness.

Quantitative outcome data (surveys, tests, measures) and qualitative data (interviews, observations)

Palinkas, L. A., Aarons, G. A., Horwitz, S., Chamberlain, P., Hurlburt, M., & Landsverk, J. (2011). Mixed method designs in implementation research. Administration and Policy in Mental Health and Mental Health Services Research, 38(1), 44–53.

convergent-mixed-methods sequential-explanatory-mixed-methods sequential-exploratory-mixed-methods randomized-controlled-trial program-evaluation embedded-mixed-methods


Evaluation-Focused Legal Content Analysis

assessment-oriented legal content analysis

Late 20th century; evaluation-focused applications emerged prominently from the 1990s onward

Builds on Klaus Krippendorff's content analysis framework and legal scholarship traditions

Evaluation-focused legal content analysis is a systematic method for examining legal texts — statutes, regulations, court decisions, contracts, or policy documents — with an explicit evaluative purpose: to assess whether and how well legal instruments achieve specified goals, standards, or values. It combines the structured coding procedures of content analysis with normative legal evaluation criteria, enabling researchers and practitioners to make evidence-based assessments of legal effectiveness, compliance, or quality.

Systematic qualitative/quantitative legal document analysis

Legal texts (statutes, regulations, court decisions, contracts, policy documents)

Krippendorff, K. (2004). Content Analysis: An Introduction to Its Methodology (2nd ed.). Sage Publications.

Nourse, V., & Schacter, J. (2002). The Politics of Legislative Drafting: A Congressional Case Study. New York University Law Review, 77(3), 575–624.

legal-content-analysis doctrinal-legal-research comparative-legal-analysis program-evaluation policy-analysis document-analysis


Evaluation-Focused Multiphase Mixed Methods Design

evaluation multiphase mixed methods
program evaluation multiphase design
mixed methods program evaluation
evaluative multiphase design

2007–2009 (multiphase design formalized 2007; evaluation-focused applications consolidated ca. 2009)

John W. Creswell & Vicki L. Plano Clark (multiphase design); Stewart I. Donaldson and colleagues (evaluation-focused framing)

The evaluation-focused multiphase mixed methods design applies the multiphase mixed methods framework explicitly to program evaluation contexts, orchestrating three or more sequential or iterative phases — each drawing on quantitative measures, qualitative inquiry, or both — to assess a program, policy, or intervention from needs assessment through impact evaluation. An overarching evaluation question unifies all phases, and findings from each phase directly shape the evaluation questions and methods of the next.

Mixed methods research design — program evaluation variant

Quantitative data (surveys, outcome measures, administrative records) and qualitative data (interviews, documents, observations) collected and integrated across three or more evaluation phases

Donaldson, S. I., Christie, C. A., & Mark, M. M. (Eds.). (2009). What Counts as Credible Evidence in Applied Research and Evaluation Practice? Sage.


Evaluation-oriented mixed methods matrix

Evaluation-Oriented Mixed Methods Matrix

mixed methods display matrix for evaluation

2000s (Teddlie & Tashakkori 2009; Greene 2007)

Teddlie & Tashakkori (matrix framework); Greene (evaluation-oriented framing)

The evaluation-oriented mixed methods matrix is a structured planning and display tool applied within program evaluation contexts. It maps evaluation questions against data sources, timing, and method types — quantitative and qualitative — in a grid format, making the integration logic explicit and auditable. Rooted in Greene's value-engaged mixed methods tradition and Teddlie and Tashakkori's matrix framework, it serves evaluators who must justify methodological choices to stakeholders while addressing multiple evaluation purposes simultaneously.

Quantitative and qualitative data collected within a program evaluation context

Teddlie, C., & Tashakkori, A. (2009). Foundations of Mixed Methods Research: Integrating Quantitative and Qualitative Approaches in the Social and Behavioral Sciences. Sage.


Evaluation-Oriented Mixed Methods Meta-Inference

evaluation MMR meta-inference
evaluation-focused meta-inference
mixed methods evaluation inference
meta-inference in evaluation research

Abbas Tashakkori and Charles Teddlie (meta-inference); evaluation-oriented framing associated with Donna M. Mertens and Jennifer C. Greene

Evaluation-oriented mixed methods meta-inference is a rigorous concluding process in program evaluation research in which the researcher integrates inferences drawn from both quantitative and qualitative strands of a mixed methods study into a single, coherent, higher-order conclusion. This meta-inference is explicitly anchored to evaluation questions — such as program worth, merit, or impact — and is judged by dual quality criteria: inferential consistency and interpretive consistency across strands.

Quantitative data (surveys, tests, instruments) and qualitative data (interviews, observations, documents)

Tashakkori, A., & Teddlie, C. (Eds.). (2010). SAGE Handbook of Mixed Methods in Social and Behavioral Research (2nd ed.). SAGE Publications.


Evaluation-Oriented Multilevel Mixed Methods Design

multilevel mixed methods evaluation
hierarchical mixed methods evaluation
MLM mixed methods
nested mixed methods evaluation

Donna M. Mertens; John W. Creswell & Vicki L. Plano Clark (systematization)

Evaluation-oriented multilevel mixed methods is a research design that combines quantitative and qualitative data across hierarchically nested levels of an organization or system — such as students within classrooms within schools — to evaluate a program, policy, or intervention. By capturing outcomes, processes, and contextual factors simultaneously at each level, this design produces richer evaluative inferences than either purely statistical multilevel models or single-level qualitative evaluations alone.

Nested/hierarchical quantitative data plus qualitative data at multiple organizational levels

Mertens, D. M. (2010). Research and Evaluation in Education and Psychology: Integrating Diversity with Quantitative, Qualitative, and Mixed Methods (3rd ed.). Sage.

mixed-methods-research multilevel-modeling program-evaluation convergent-mixed-methods hierarchical-linear-modeling transformative-mixed-methods


Evaluation-oriented Pragmatic Mixed Methods Design

pragmatic evaluation mixed methods
program evaluation mixed methods
applied pragmatic mixed methods
mixed methods program evaluation

Jennifer C. Greene; Abbas Tashakkori & Charles Teddlie

Evaluation-oriented pragmatic mixed methods is a research design that combines quantitative and qualitative data collection within a pragmatist philosophical stance, expressly to evaluate programs, policies, or interventions. Rather than adhering rigidly to a single paradigm, it selects methods for their fitness to answer evaluation questions about program effectiveness, outcomes, and stakeholder experiences. The design is widely applied in education, public health, social services, and development evaluation contexts.

Quantitative and qualitative data (surveys, tests, interviews, documents, observations)

Tashakkori, A., & Teddlie, C. (Eds.). (2010). SAGE Handbook of Mixed Methods in Social and Behavioral Research (2nd ed.). Sage.

convergent-mixed-methods sequential-explanatory-mixed-methods sequential-exploratory-mixed-methods program-evaluation action-research realist-evaluation


Evaluation-oriented qualitative-priority mixed methods design

Evaluation-Oriented Qualitative-Priority Mixed Methods Design

qualitative-dominant evaluation mixed methods

mixed methods evaluation with qualitative strand

1989–2007 (formalized in evaluation and mixed methods literature)

An evaluation-oriented qualitative-priority mixed methods design places qualitative data collection and analysis at the center of a program or policy evaluation, while selectively incorporating quantitative data to corroborate, contextualize, or extend qualitative findings. The design is guided by an evaluative purpose — assessing merit, worth, or significance of a program — with the qualitative strand carrying the primary interpretive weight and quantitative evidence serving a supplementary role.

Qualitative data primary (interviews, observations, documents); quantitative data secondary (surveys, administrative records)

exploratory-sequential-mixed-methods explanatory-sequential-mixed-methods convergent-mixed-methods program-evaluation qualitative-case-study-evaluation transformative-mixed-methods


Evaluation-oriented quantitative-priority mixed methods design

Evaluation-Oriented Quantitative-Priority Mixed Methods Design

evaluation mixed methods with quantitative priority

Teddlie & Tashakkori; Creswell & Plano Clark

An evaluation-oriented quantitative-priority mixed methods design applies mixed methods inquiry within an evaluation context, where the primary purpose is judging a program, policy, or intervention. Quantitative data carry the greater evidential weight — measuring outcomes, effectiveness, and reach — while qualitative data serve as a secondary, explanatory strand that contextualizes and deepens interpretation of the quantitative findings.

Quantitative (primary) and qualitative (secondary) data


EVAMIX

EVAMIX (EVAluation of MIXed data) is a ranking multi-criteria decision-making (MCDM) method introduced by Voogd, H. in 1983. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Mixed ordinal+cardinal pairwise dominance aggregation

Voogd, H. (1983). Multicriteria Evaluation for Urban and Regional Planning. Pion, London

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


Event Detection

event extraction
Olay Tespiti (Event Detection)

Event detection is a natural-language-processing information-extraction task that finds events, historical developments, and action expressions in text and classifies them by type. It grew out of the Automatic Content Extraction (ACE) program described by Doddington et al. (2004) and is widely used in news analysis and historical research.

Detect and classify event mentions in text

Named-entity recognition (NER) and semantic role labeling (SRL)

Doddington, G. et al. (2004). The Automatic Content Extraction (ACE) Program — Tasks, Data, and Evaluation. LREC.

Chen, Y. & Ng, V. (2012). Joint Modeling for Chinese Event Extraction with Rich Linguistic Features. COLING.

named-entity-recognition semantic-role-labeling text-classification sentiment-analysis


Event-Related Potential Analysis

Event-Related Potential (ERP) analysis is a method for extracting stereotyped brain electrical responses time-locked to stimulus presentation or behavioral events from EEG recordings. Formalized in the cognitive neuroscience literature by researchers including Sutherland and Picton, ERP analysis enables millisecond-level temporal resolution of neural processing and has become foundational for studying perception, attention, memory, and decision-making.

Luck, S. J. (2005). An Introduction to the Event-Related Potential Technique. MIT Press.

Picton, T. W., Bentin, S., Berg, P., et al. (2000). Guidelines for using human event-related potentials to study cognition: recording standards and publication criteria. Psychophysiology, 37(2), 127–152.


Event Study Design

Event Study Tasarımı (Nedensel Olay Çalışması)

Sun & Abraham (2021); Callaway & Sant'Anna (2021)

The event study design is a generalised difference-in-differences model that estimates a separate treatment-effect coefficient for each period before and after an intervention, tracing the dynamics of the effect over event time. Its modern, heterogeneity-robust form was developed by Sun & Abraham (2021) and Callaway & Sant'Anna (2021).

Two-way fixed effects with event-time leads and lags (cluster-robust)

Sun, L. & Abraham, S. (2021). Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects. Journal of Econometrics, 225(2), 175–199.

Callaway, B. & Sant'Anna, P. H. C. (2021). Difference-in-Differences with Multiple Time Periods. Journal of Econometrics, 225(2), 200–230.

did-staggered interrupted-time-series panel-fixed-effects regression-discontinuity shift-share-iv


Event Study Design in Education Research

Event Study Design for Causal Inference in Education Research

event study
education event study
policy event study
dynamic difference-in-differences

1993 (general); 2000s–2010s (education applications)

Jacobson, LaLonde & Sullivan (1993); popularized in education by Lafortune, Rothstein & Schanzenbach (2018) and subsequent education-policy literature

An event study design tracks how educational outcomes evolve before and after a clearly defined event — such as a school finance reform, accountability policy, or curriculum change — for affected and unaffected units. By estimating period-by-period treatment effects relative to a baseline period, it delivers both a causal estimate of the policy's impact and a transparent test of the parallel-trends assumption underpinning difference-in-differences.

Panel data with administrative or survey records (test scores, enrollment, attainment)

Jacobson, L. S., LaLonde, R. J., & Sullivan, D. G. (1993). Earnings Losses of Displaced Workers. American Economic Review, 83(4), 685-709.

Lafortune, J., Rothstein, J., & Schanzenbach, D. W. (2018). School Finance Reform and the Distribution of Student Achievement. American Economic Journal: Applied Economics, 10(2), 1-26.

difference-in-differences panel-fixed-effects regression-discontinuity-design interrupted-time-series synthetic-control instrumental-variables


Event Study

Olay Çalışması (Event Study — CAR, BHAR)

MacKinlay (review); Kothari & Warner (econometrics)

The event study is a financial research method that measures the impact of a news release, policy change, or corporate event on asset prices through cumulative abnormal returns. Reviewed by MacKinlay (1997) and formalised econometrically by Kothari and Warner (2007), it is the standard tool for testing the efficient-market hypothesis and analysing the information content of events.

Abnormal-return model for financial events

Market model / Fama-French expected returns; cumulated abnormal returns

MacKinlay, A. C. (1997). Event Studies in Economics and Finance. Journal of Economic Literature, 35(1), 13–39.

Kothari, S. P., & Warner, J. B. (2007). Econometrics of Event Studies. In B. E. Eckbo (Ed.), Handbook of Corporate Finance: Empirical Corporate Finance (Vol. 1, pp. 3–36). Elsevier.

ols-regression fama-french-three-factor backtesting-var high-frequency-microstructure liquidity-risk-models


Event Tree Analysis

ETA
Event Sequence Diagram Analysis
Initiating Event Analysis
Olay Ağacı Analizi

Event Tree Analysis (ETA) is a forward inductive technique used in reliability and risk engineering to model the possible outcomes that follow an initiating event. Starting from a single undesired event, ETA traces all subsequent event sequences through a binary branching tree representing the success or failure of safety barriers and protective systems. Introduced formally in reliability and risk literature by Andrews and Moss (2002), it is widely applied in nuclear, chemical, and aerospace industries to quantify accident sequence probabilities and guide safety decision-making.

Output: Accident sequence probabilities

Input: Initiating event + safety barrier reliabilities

Andrews, J. D., & Moss, T. R. (2002). Reliability and Risk Assessment (2nd ed.). Professional Engineering Publishing.


Evidence-Based Practice Attitude Scale (36-item)

EBPAS
EBPAS-36
Evidence-Based Practice Attitude

The EBPAS-36 is a 36-item self-report questionnaire that assesses clinicians' and organizational leaders' attitudes toward adopting and implementing evidence-based practices (EBP). Developed by Aarons in 2005 and refined through multiple validation studies, it measures four core dimensions: perceived requirements to adopt EBP, the appeal and usefulness of EBP to individual practice, organizational openness to innovation, and perceived divergence between current practice and EBP requirements. The EBPAS is widely used in healthcare, mental health, child welfare, and substance abuse treatment settings to predict adoption readiness and guide implementation planning.

Aarons, G. A. (2011). Evidence-Based Practice Attitude Scale-50 (EBPAS-50) and EBPAS-36 short form: Psychometric properties. Implementation Science, 6(1), 89.

implementation-leadership-scale implementation-climate-scale organisational-readiness-change stages-of-concern-questionnaire knowledge-to-action-scale


Evolutionary Game Theory

Evolutionary Game Theory with Replicator Dynamics

ESS
Evolutionarily Stable Strategy
Replicator Dynamics

Evolutionary Game Theory applies game-theoretic reasoning to biological evolution and social dynamics, where populations of agents with different strategies interact repeatedly. Introduced by John Maynard Smith and George Price in 1973, the framework uses the concept of Evolutionarily Stable Strategies (ESS) to identify strategy distributions that cannot be invaded by mutant strategies. Replicator dynamics describe how strategy frequencies evolve over time when reproduction is proportional to payoff success.

Smith, J. M., & Price, G. R. (1973). The logic of animal conflict. Nature, 246(5427), 15-18.

Maynard Smith, J. (1982). Evolution and the Theory of Games. Cambridge University Press.

nash-equilibrium bayesian-nash-equilibrium cournot-competition stackelberg-competition


Evolutionary Strategy

Covariance Matrix Adaptation Evolution Strategy (CMA-ES)

CMA-ES
Evolution Strategy
Evrimsel Strateji (CMA-ES)
self-adapting evolution strategy

CMA-ES, short for Covariance Matrix Adaptation Evolution Strategy, is a modern derivative-free optimizer for continuous black-box functions introduced by Hansen and Ostermeier in 2001. It maintains a population of candidate solutions drawn from a multivariate normal distribution and iteratively updates the distribution's mean, step size, and full covariance matrix to steer the search toward better regions of the parameter space. It has become the de-facto standard for continuous black-box optimization and is widely used in neural architecture search and reinforcement-learning policy optimization.

Derivative-free continuous black-box optimizer

Continuous, 10–1000 dimensions (ideal range)

Hansen, N. & Ostermeier, A. (2001). Completely Derandomized Self-Adaptation in Evolutionary Strategies. Evolutionary Computation, 9(2), 159-195.

Hansen, N. (2016). The CMA Evolution Strategy: A Tutorial. arXiv:1604.00772.

genetic-algorithm particle-swarm-optimization bayesian-optimization surrogate-optimization robust-optimization


EWMA Chart

Exponentially Weighted Moving Average (EWMA) Control Chart

exponentially weighted moving average chart

The exponentially weighted moving average (EWMA) control chart, introduced by S. W. Roberts in 1959, monitors a process using a weighted average that gives the most recent observation the greatest weight while letting older observations fade geometrically. Like CUSUM, this memory makes it highly effective at detecting small, sustained shifts in the process mean, with a single smoothing parameter λ controlling how much past information the chart retains.

Statistical process control chart for small shifts

Exponentially weighted moving average of the process

Roberts, S. W. (1959). Control chart tests based on geometric moving averages. Technometrics, 1(3), 239–250.

Montgomery, D. C. (2009). Introduction to Statistical Quality Control (6th ed.). John Wiley & Sons.

shewhart-control-chart cusum-chart attributes-control-chart exponential-smoothing


Ex Post Facto Design

after-the-fact research
retrospective non-experimental design
causal-comparative design
EPF design

1960s (systematic codification); concept used in social science from early 20th century

Formalized by Fred N. Kerlinger; foundational treatment by Donald T. Campbell and Julian C. Stanley

Ex post facto design is a non-experimental quantitative research approach in which the researcher investigates a phenomenon after it has already occurred, examining pre-existing differences between groups to explore potential causal or associative relationships. Because the independent variable cannot be manipulated — it happened in the past — the design relies on careful group selection, retrospective data collection, and statistical controls to approximate causal inference without experimental intervention.

Non-experimental quantitative research design

Existing records, archival data, survey responses, secondary datasets

Kerlinger, F. N. (1964). Foundations of Behavioral Research. Holt, Rinehart and Winston.

Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research. Rand McNally.

causal-comparative-research correlational-research longitudinal-research cohort-research descriptive-research quasi-experimental-design


EXAFS

EXAFS spectroscopy
X-ray absorption spectroscopy

Extended X-ray Absorption Fine Structure (EXAFS) is a synchrotron-based X-ray spectroscopy technique that measures the local geometric and electronic structure around a specific atom in any material, crystal or amorphous. Discovered by Sayers, Stern, and Lytle in 1971, EXAFS reveals interatomic distances, coordination numbers, and disorder in the atomic environment by analyzing oscillations in the X-ray absorption spectrum above an absorption edge.

Sayers, D. E., Stern, E. A., & Lytle, F. W. (1971). New technique for investigating noncrystalline structures: Fourier analysis of the extended X-ray absorption fine structure. Physical Review Letters, 27(18), 1204-1207.

Stern, E. A., Sayers, D. E., & Lytle, F. W. (1975). Extended x-ray-absorption-fine-structure technique. Physical Review B, 11(12), 4836-4846.


Exercise Addiction Inventory

Adrian Terry, Attila Szabo, Mark Griffiths

The EAI is a 6-item questionnaire measuring the risk of exercise addiction or exercise dependence—the compulsive continuation of exercise despite negative consequences and in response to withdrawal anxiety. Developed by Terry, Szabo, and Griffiths in 2004, the EAI is a brief, practical screening tool for identifying athletes and exercisers at risk for pathological exercise patterns that compromise physical health and psychological wellbeing.

Self-report exercise addiction/dependence screening questionnaire

Terry, A., Szabo, A., & Griffiths, M. D. (2004). The exercise addiction inventory: A new brief screening tool. British Journal of Sports Medicine, 38(4), 558–561.

Grubbs, J. B., & Grubbs, R. R. (2015). Exercise addiction. In V. R. Preedy (Ed.), Neuropathology of Drug Addictions and Substance Misuse (pp. 750–758). Academic Press.


Exercise Self-Efficacy Scale

Exercise Confidence Scale
Physical Activity Self-Efficacy

Albert Bandura; validated by Resnick & Jenkins

The Exercise Self-Efficacy Scale measures an individual's confidence in their ability to exercise regularly and maintain physical activity despite challenges. Grounded in Albert Bandura's Social Cognitive Theory, self-efficacy is the belief that one has the capability to execute a specific behavior and achieve desired outcomes. For exercise, self-efficacy encompasses confidence in overcoming barriers (time, fatigue, weather), maintaining consistency, and managing setbacks or relapse. Research consistently demonstrates that exercise self-efficacy is one of the strongest predictors of exercise adherence; individuals with high confidence are more likely to initiate exercise, persist through difficulties, and maintain activity over time. The scale is widely used in primary care, cardiac and pulmonary rehabilitation, weight management, diabetes care, and exercise research to assess readiness for behavior change and to evaluate interventions designed to boost confidence.

Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman.

Resnick, B., & Jenkins, L. S. (2000). Testing the reliability and validity of the Self-Efficacy for Exercise Scale. Nursing Research, 49(3), 154-159.


Exergoeconomic Analysis

Exergoeconomic Analysis for Thermal Systems

Exergoeconomic analysis combines thermodynamics and economics by assigning monetary costs to exergy streams. It reveals how thermodynamic irreversibilities translate into economic losses within industrial systems. This approach enables engineers to identify the most economically significant inefficiencies and make informed decisions about component improvements and system optimization.

Tsatsaronis, G. (1993). Thermoeconomic analysis and optimization of energy conversion processes. Progress in Energy and Combustion Science, 19(4), 323-356.

Lozano, M. A., & Valero, A. (1994). Theory of exergetic cost. Energy Policy, 21(12), 1109-1140.


Exergoenvironmental Analysis

Exergoenvironmental Analysis for Sustainable Thermal Systems

environmental exergy costing
exergy-based LCA

Exergoenvironmental analysis extends exergy-based methods to quantify and allocate environmental impacts of thermal systems. It assigns environmental costs to exergy streams based on upstream lifecycle impacts, revealing which components contribute most significantly to environmental burdens. This enables engineers to design sustainable energy systems by optimizing the trade-off between thermodynamic and environmental performance.

Meyer, L., Tsatsaronis, G., Buchgeister, J., & Schebek, L. (2009). Exergoenvironmental analysis for evaluation of the environmental impact of energy conversion processes. Energy, 34(1), 75-89.

Valero, A., Torres, C., Valle-Zermeño, R., Gantiva-Rodriguez, R., Botero, E., Lozano, M. A., & Ospina-Alarcón, M. (2016). On the unification of LCA and exergy analysis as complementary tools. Resources, Conservation and Recycling, 107, 58-75.


Exergy Analysis

Available Work Analysis
Availability Analysis
Second-Law Analysis
Ekserji Analizi

Exergy analysis is a thermodynamic method that quantifies the maximum useful work obtainable from an energy carrier relative to a reference dead state, revealing where and how irreversibilities destroy quality energy. Formally linked to sustainable development by Marc Rosen and Ibrahim Dincer in 2001, it extends the first-law energy balance with second-law accounting to expose true thermodynamic inefficiencies that conventional energy audits miss.

Rosen, M. A., & Dincer, I. (2001). Exergy as the confluence of energy, environment and sustainable development. Exergy, An International Journal, 1(1), 3–13.


Exhaustion and Disengagement Scale

The Exhaustion and Disengagement Scale (EDIS), based on work by Shirom and colleagues, is a brief burnout assessment tool measuring two core dimensions of occupational burnout: emotional, physical, and cognitive exhaustion, and psychological disengagement from work. Developed in the early 2000s, the EDIS emphasizes the depletion and withdrawal that characterize burnout, with particular attention to physiologic and cognitive fatigue rather than interpersonal dimensions. It is widely used in occupational health research, particularly in European and Israeli occupational health contexts.

Shirom, A., Melamed, S., Toker, S., Berliner, S., & Shapira, I. (2005). Burnout, vigor, and physical health among healthcare workers. Psychology and Health, 20(6), 769-785.

Shirom, A. (1989). Burnout in work organizations. In C. L. Cooper & I. T. Robertson (Eds.), International Review of Industrial and Organizational Psychology, Vol. 4 (pp. 25-48). Chichester: Wiley.

copenhagen-burnout-inventory oldenburg-burnout-inventory effort-reward-imbalance-scale recovery-experience-questionnaire work-related-burnout-scale


Burnout Exhaustion Scale

Single-Item Exhaustion Scale / Exhaustion Thermometer

Exhaustion Item
Work-Related Exhaustion
Fatigue Scale

Multiple researchers; derived from comprehensive burnout instruments

The Exhaustion Scale is a brief, single-item or multi-item measure of work-related exhaustion and fatigue. Derived from comprehensive burnout instruments such as the Maslach Burnout Inventory and Copenhagen Burnout Inventory, the Exhaustion Scale isolates the depletion dimension as a rapid screening tool. It is particularly useful in occupational health surveillance, longitudinal monitoring, and research when comprehensive multi-dimensional burnout assessment is impractical due to time or response burden constraints.

Lundgren-Nilsson, A., Jonsdottir, H., Pallesen, S., & Rask, A. M. (2012). Burnout is more strongly associated with psychic strain in women than in men. Journal of Nursing Management, 20(1), 112-121.

Awa, W. L., Plaumann, M., & Walter, U. (2010). Burnout prevention: A review of intervention programs. Patient Education and Counseling, 78(2), 184-190.

copenhagen-burnout-inventory oldenburg-burnout-inventory exhaustion-disengagement-scale work-related-burnout-scale recovery-experience-questionnaire


EWB Scale

Raymond F. Paloutzian & Craig W. Ellison

The Existential Well-Being Scale (EWB), developed by Paloutzian and Ellison in 1982, is a 10-item self-report measure of existential meaning and well-being: the sense that one's life has purpose, direction, and intrinsic value. Derived from the larger Spiritual Well-Being Scale (which includes religious well-being), the EWB focuses on the secular dimension of well-being—not faith or religious conviction, but existential satisfaction and sense of purpose. It has become widely used in psychology and health research to assess meaning, life satisfaction, and resilience factors protective against depression, anxiety, and suicide.

Paloutzian, R. F., & Ellison, C. W. (1982). Loneliness, spiritual well-being, and the quality of life. In L. A. Peplau & D. Perlman (Eds.), Loneliness: A Sourcebook of Current Theory, Research and Therapy (pp. 224–237). Wiley. ISBN: 9780471084846.


Exoplanet Transmission Spectroscopy

Transmission Spectroscopy for Exoplanet Atmosphere Characterization

Transmission Spectrum
Atmospheric Spectroscopy
Transit Spectroscopy

Transmission spectroscopy is a technique for studying the atmospheres of exoplanets by analyzing the light passing through the planetary atmosphere during transit. Pioneered by David Charbonneau in 2002 with the detection of sodium in HD 209458b's atmosphere, this method has become the primary tool for characterizing exoplanet atmospheres and searching for biosignatures.

Charbonneau, D., Brown, T. M., Noyes, R. W., & Gilliland, R. L. (2002). Detection of an atmospheric trace constituent in the transmission spectrum of a distant extrasolar planet. Astrophysical Journal, 568(1), 377-384.

Kreidberg, L., et al. (2014). A precise water abundance measurement for the hot Jupiter WASP-43b. Astrophysical Journal Letters, 793(2), L15.

Sing, D. K., et al. (2016). The atmospheric circulation of hot Jupiters. Nature, 529(7584), 59-62.


Expectation Propagation

Expectation Propagation for Approximate Bayesian Inference

Expectation Propagation (EP) is a deterministic message-passing algorithm for approximate posterior inference in Bayesian models, introduced by Thomas P. Minka at UAI 2001. It iteratively refines a set of local approximate factors — each drawn from the exponential family — so that their product closely matches the true intractable posterior, achieving higher accuracy than mean-field variational inference on many probabilistic machine learning tasks.

Posterior approximation / probabilistic machine learning

Continuous and/or discrete latent variables

Deterministic message-passing (moment matching)

Approximate posterior distribution (exponential family)

Minka, T. P. (2001). Expectation propagation for approximate Bayesian inference. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI-01), pp. 362–369. Morgan Kaufmann.

Minka, T. P. (2001/2013). Expectation propagation for approximate Bayesian inference. arXiv:1301.2294 [cs.AI].

Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. (Chapter 10: Approximate Inference; Section 10.7 covers Expectation Propagation.)

variational-inference mean-field-vi laplace-approximation belief-propagation mcmc assumed-density-filtering


Expert Interview

elite interview
key informant interview
specialist interview

Meuser & Nagel (methodological codification, 1991); roots in elite interview tradition (Dexter, 1970)

The expert interview is a qualitative method in which researchers conduct in-depth, semi-structured conversations with individuals who hold specialised knowledge, experience, or decision-making authority in a defined field. Unlike general population interviews that target subjective lived experience, expert interviews treat respondents as proxies for a broader institutional or professional knowledge domain. The method is widely used in policy research, organisational studies, science and technology studies, and applied social sciences to map tacit professional knowledge, reconstruct decision processes, and triangulate documentary sources.

Semi-structured or unstructured interview transcripts (text data)

Bogner, A., Littig, B., & Menz, W. (Eds.). (2009). Interviewing Experts. Palgrave Macmillan.

Meuser, M., & Nagel, U. (1991). ExpertInneninterviews — vielfach erprobt, wenig bedacht. In D. Garz & K. Kraimer (Eds.), Qualitativ-empirische Sozialforschung (pp. 441–471). Westdeutscher Verlag.

delphi-method case-study grounded-theory focus-group phenomenology action-research


Explainable Association Rules

XAI association rules
interpretable association rules
rule-based explanation mining
transparent association rule learning

Agrawal, R., Imielinski, T., & Swami, A. (foundational); XAI framing: broader community (2010s–present)

Explainable Association Rules leverages the inherently symbolic, if-then structure of association rule mining to provide human-readable explanations of data patterns or black-box model decisions. Because each rule explicitly states its antecedent and consequent together with support, confidence, and lift, the outputs are natively interpretable without requiring a secondary post-hoc surrogate.

Interpretable pattern mining / XAI technique

Transactional, categorical, or binary feature data

Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, 207–216.

Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019). Definitions, methods, and applications in interpretable machine learning. Proceedings of the National Academy of Sciences, 116(44), 22071–22080.

association-rules apriori-algorithm explainable-decision-tree explainable-random-forest fp-growth explainable-naive-bayes


Explainable Autoencoder-Based Anomaly Detection (XAI-augmented Reconstruction Error)

interpretable autoencoder anomaly detection

Combination of autoencoder anomaly detection (Hinton & Salakhutdinov, 2006) and XAI methods (e.g., Lundberg & Lee, 2017)

Explainable Autoencoder Anomaly Detection augments a standard autoencoder-based anomaly detector with an interpretability layer — such as SHAP values or feature-wise reconstruction error decomposition — that identifies which input features drove the anomaly flag for each observation, turning an opaque reconstruction-error score into an actionable, human-readable explanation.

Unsupervised anomaly detection with post-hoc or intrinsic explainability

Continuous, tabular, or high-dimensional numeric data

Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30.

Chalapathy, R., & Chawla, S. (2019). Deep learning for anomaly detection: A survey. arXiv preprint arXiv:1901.03407.


Explainable BERT-based Classification

Explainable BERT-based Text Classification

XAI-BERT
interpretable BERT classifier
BERT with post-hoc explanation
transparent BERT classification

Devlin et al. (BERT); explainability methods by Lundberg & Lee (SHAP), Ribeiro et al. (LIME), Sundararajan et al. (Integrated Gradients)

Explainable BERT-based Classification combines the predictive power of fine-tuned BERT transformers for text classification with post-hoc or intrinsic explainability techniques — such as SHAP, LIME, attention analysis, or integrated gradients — to reveal which words or tokens drove each prediction. The result is a classifier that is both accurate and interpretable enough for high-stakes or auditable NLP applications.

Pre-trained transformer classifier with post-hoc or intrinsic explainability

Text (documents, sentences, social media, reviews)

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of NAACL-HLT 2019, pp. 4171–4186.

Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems (NeurIPS), 30, 4765–4774.

bert-based-classification roberta-based-classification explainable-transformer explainable-recurrent-neural-network sentence-embeddings fine-tuned-bert-based-classification


Explainable DBSCAN

Explainable Density-Based Spatial Clustering of Applications with Noise

XAI-DBSCAN
interpretable DBSCAN
transparent density clustering
DBSCAN with post-hoc explanation

Ester, M. et al. (DBSCAN); XAI layer via Lundberg & Lee (SHAP)

Explainable DBSCAN pairs the DBSCAN density-based clustering algorithm with post-hoc interpretability methods — most commonly SHAP values or local surrogate models — to reveal which input features drive the algorithm's cluster and noise assignments. It enables analysts to understand why specific points were grouped together or flagged as outliers, bridging the gap between powerful density-based partitioning and human-readable explanation.

Unsupervised clustering with post-hoc interpretability

Continuous or mixed tabular data; spatial coordinates

Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), 226–231. AAAI Press.

Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30. Curran Associates.

dbscan hdbscan k-means gaussian-mixture-model explainable-k-nearest-neighbors explainable-isolation-forest


Explainable Decision Tree

Explainable Decision Tree (Interpretable Rule-Based Classification and Regression Tree)

XDT
interpretable decision tree
rule-based decision tree
transparent decision tree

1984 (CART); XAI framing formalized 2010s–2020s

Breiman, L.; Friedman, J.; Olshen, R. A.; Stone, C. J.

An Explainable Decision Tree is a classification or regression tree deliberately grown to be shallow, readable, and auditable — producing a finite set of if-then rules that a human can verify without additional tools. It sits at the intersection of predictive modelling and Explainable AI (XAI), chosen when stakeholders must understand and trust every prediction the model makes.

Tabular (continuous, categorical, mixed)

Breiman, L., Friedman, J., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees. Wadsworth & Brooks/Cole.

Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215.

random-forest decision-tree xgboost logistic-regression rule-based-classifier shap-explainability


Explainable Diffusion Model

Explainable Diffusion Model (XAI-Augmented Denoising Diffusion Probabilistic Model)

XAI-DDPM
interpretable diffusion model
transparent diffusion model
explainable DDPM

Ho, J., Jain, A., & Abbeel, P. (DDPM, 2020); XAI augmentation by subsequent researchers

An Explainable Diffusion Model couples a denoising diffusion probabilistic model with post-hoc or intrinsic explainability techniques — such as SHAP, gradient-based saliency, attention analysis, or concept-based probing — so that each generative or predictive decision can be audited and justified rather than treated as a black box.

Generative model with post-hoc or intrinsic explainability

Images, audio, tabular, multimodal continuous data

Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems, 33, 6840–6851.

fine-tuned-diffusion-model self-supervised-diffusion-model explainable-variational-autoencoder explainable-gan explainable-vision-transformer multimodal-diffusion-model


Explainable Extra Trees

Explainable Extremely Randomized Trees (Extra Trees with Post-Hoc Interpretability)

XAI-ET
Explainable ET
Interpretable Extra Trees
Extra Trees with SHAP

2006 (Extra Trees); 2017 (SHAP integration)

Geurts, P., Ernst, D., Wehenkel, L. (Extra Trees); Lundberg, S. M. (SHAP explainability layer)

Explainable Extra Trees combines the Extremely Randomized Trees (Extra Trees) ensemble algorithm with post-hoc explainability methods — most commonly SHAP values — to deliver both strong predictive performance and transparent, feature-level explanations. It extends the classic Extra Trees classifier or regressor so that every prediction can be decomposed into individual feature contributions, satisfying demands for accountability in applied and regulated domains.

Ensemble (randomized trees) with post-hoc explainability

Tabular (continuous, categorical, mixed features)

Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42.

random-forest xgboost decision-tree shap-explainability gradient-boosting extra-trees


Explainable FP-Growth

Explainable Frequent Pattern Growth (XAI-Augmented FP-Growth)

XAI-FP-Growth
interpretable frequent pattern mining
explainable frequent itemset mining
transparent FP-Growth

2000 (FP-Growth); XAI augmentation emerged ~2018–present

Han, J., Pei, J., & Yin, Y. (FP-Growth); XAI augmentation from the interpretable ML community

Explainable FP-Growth augments the classic FP-Growth frequent-pattern mining algorithm with post-hoc interpretability tools — such as rule importance scores, visual pattern trees, and counterfactual explanations — so analysts can not only discover frequent itemsets and association rules but also understand why specific patterns matter, which items drive rule confidence, and how to communicate findings transparently to stakeholders.

Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.

apriori-algorithm association-rules fp-growth explainable-association-rules explainable-apriori-algorithm semi-supervised-fp-growth


Explainable GAN

Explainable Generative Adversarial Network

XAI-GAN
Interpretable GAN
Transparent GAN
Explainable Generative Model

Bau, D. et al. (GAN Dissection); broader XAI-GAN community

Explainable GAN applies interpretability techniques to Generative Adversarial Networks to reveal which internal units and latent directions cause specific visual or structural features in generated outputs. It combines GAN training with post-hoc analysis tools — such as unit dissection, saliency maps, or disentangled latent spaces — to make generative model behaviour transparent and auditable.

Images, structured data, or other high-dimensional distributions

Bau, D., Zhu, J.-Y., Strobelt, H., Zhou, B., Tenenbaum, J. B., Freeman, W. T., & Torralba, A. (2019). GAN Dissection: Visualizing and Understanding Generative Adversarial Networks. In Proceedings of the International Conference on Learning Representations (ICLR 2019).

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative Adversarial Nets. In Advances in Neural Information Processing Systems (NeurIPS 2014), 27.


Explainable Gaussian Mixture Model

X-GMM
Interpretable GMM
Explainable GMM
Transparent Gaussian Mixture Model

Reynolds, D. A. & Rose, R. C. (GMM); explainability extensions by various authors

An Explainable Gaussian Mixture Model (X-GMM) augments the classical GMM probabilistic clustering framework with transparency mechanisms — such as feature-attribution scores, component-level summaries, or sparse covariance structures — so that discovered clusters and density estimates can be understood, communicated, and audited by human experts.

Probabilistic clustering with post-hoc or built-in explainability

Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective (Ch. 11 — Mixture Models). MIT Press.

gaussian-mixture-model k-means-clustering expectation-maximization shap variational-autoencoder latent-class-analysis


Explainable Gaussian Process

Explainable Gaussian Process Regression and Classification

XAI-GP
interpretable Gaussian process
explainable GP
transparent Gaussian process

Rasmussen, C. E. & Williams, C. K. I. (GP); XAI layer via Lundberg & Lee (SHAP, 2017) and others

An Explainable Gaussian Process (XAI-GP) combines the probabilistic, uncertainty-aware predictions of a Gaussian Process model with systematic interpretability tools — such as SHAP values, kernel decomposition, or sensitivity analysis — so that every prediction comes with both a calibrated confidence interval and an auditable explanation of which inputs drove it.

Probabilistic model with post-hoc or built-in interpretability

Continuous, binary, or multi-class tabular data

Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press.

gaussian-process explainable-random-forest explainable-gradient-boosting bayesian-gaussian-process support-vector-machine regularized-gaussian-process


Explainable Gradient Boosting

Explainable Gradient Boosting (Gradient Boosting with Post-hoc and Intrinsic Interpretability)

XGB with SHAP
interpretable gradient boosting
transparent gradient boosting
XAI gradient boosting

Lundberg, S. M. & Lee, S.-I. (TreeSHAP for tree ensembles)

Explainable Gradient Boosting combines the predictive power of gradient boosting ensembles with structured interpretability tools — principally SHAP (SHapley Additive exPlanations) — to produce models that are both highly accurate and transparently auditable. Practitioners obtain global feature rankings and individual-level explanations alongside standard performance metrics.

Tabular (continuous, categorical, ordinal, binary)

Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2, 56–67.

Molnar, C. (2022). Interpretable Machine Learning: A Guide for Making Black Box Models Explainable (2nd ed.). christophm.github.io/interpretable-ml-book/

gradient-boosting xgboost random-forest explainable-random-forest explainable-xgboost explainable-decision-tree


Explainable Graph Neural Network

Explainable Graph Neural Network (XAI-GNN)

XAI-GNN
GNN explainability
interpretable GNN
explainable GNN

Ying, Z. et al. (GNNExplainer); broader XAI-GNN field

Explainable Graph Neural Networks (XAI-GNN) combine standard GNN architectures with post-hoc or intrinsic explanation techniques that reveal which nodes, edges, and node features drove a model's prediction. Pioneered by GNNExplainer (Ying et al., 2019), the field addresses the black-box critique of GNNs and is essential wherever graph-based predictions must be trusted or audited.

Interpretability framework for graph neural networks

Graph-structured data (nodes, edges, node/edge features)

Ying, Z., Bourgeois, D., You, J., Zitnik, M., & Leskovec, J. (2019). GNNExplainer: Generating Explanations for Graph Neural Networks. Advances in Neural Information Processing Systems (NeurIPS), 32, 9240–9251.

Yuan, H., Yu, H., Gui, S., & Ji, S. (2023). Explainability in Graph Neural Networks: A Taxonomic Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(5), 5782–5799.


Explainable GRU

XAI-GRU
Interpretable GRU
GRU with explainability
Transparent GRU

Cho, K. et al. (GRU); explainability layer via Lundberg & Lee (SHAP) and Ribeiro et al. (LIME)

Explainable GRU pairs the Gated Recurrent Unit, a compact and efficient recurrent architecture, with explainability techniques such as SHAP, LIME, or attention weighting to reveal which time steps and features drove each prediction. It brings interpretability to sequential modelling without sacrificing the GRU's ability to capture temporal dependencies.

Recurrent neural network with post-hoc or attention-based interpretability

Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. Proceedings of EMNLP 2014, 1724–1734.

Lundberg, S. M., & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems (NeurIPS), 30, 4765–4774.

gated-recurrent-unit explainable-lstm long-short-term-memory explainable-recurrent-neural-network transformer explainable-transformer


Explainable HDBSCAN

Explainable Hierarchical Density-Based Spatial Clustering of Applications with Noise

XAI-HDBSCAN
Interpretable HDBSCAN
Explainable Hierarchical DBSCAN
HDBSCAN with XAI

McInnes, L.; Healy, J. (HDBSCAN); Lundberg & Lee (SHAP-based explanation)

Explainable HDBSCAN combines the hierarchical density-based clustering algorithm HDBSCAN with post-hoc explainability methods — primarily SHAP — to reveal which input features drive cluster membership and separation. It retains HDBSCAN's ability to find clusters of varying shape and density while adding a principled, auditable explanation layer.

Continuous / mixed tabular; high-dimensional feature spaces

McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205.

Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.

explainable-dbscan explainable-k-means explainable-gaussian-mixture-model hdbscan explainable-isolation-forest explainable-random-forest


Explainable Image Classification

Explainable Image Classification (XAI-augmented CNN/Transformer Classifiers)

XAI image classification
interpretable image classifier
explainable CNN
transparent image recognition

Selvaraju et al. (Grad-CAM); Ribeiro et al. (LIME)

Explainable image classification combines a deep learning image classifier — typically a CNN or Vision Transformer — with a post-hoc or intrinsic interpretability method such as Grad-CAM, LIME, or SHAP to produce visual or quantitative explanations of why the model assigned a particular label to an image. The goal is to make the classifier's decision process transparent, auditable, and trustworthy.

Post-hoc explainability applied to image classifiers

Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 618-626.

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). Why Should I Trust You?: Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135-1144.


Explainable Instance Segmentation

Explainable Instance Segmentation (XAI-augmented Mask Detection)

XAI instance segmentation
interpretable instance segmentation
transparent mask prediction
explainable Mask R-CNN

He, K. et al. (Mask R-CNN); XAI extensions by multiple authors

Explainable Instance Segmentation combines deep-learning instance segmentation models — which detect and delineate every individual object as a separate pixel mask — with post-hoc or ante-hoc explainability techniques such as GradCAM, SHAP, LIME, or attention visualization, so that each predicted mask is accompanied by evidence showing which image regions drove the model's decision.

Explainability-augmented deep learning pipeline

Image data (RGB, medical, satellite, industrial)

Lindner, M., Meng, C., & Bischl, B. (2023). Explaining Instance Segmentation Models via Saliency Maps and Occlusion. IEEE Transactions on Pattern Analysis and Machine Intelligence.

instance-segmentation explainable-semantic-segmentation explainable-object-detection explainable-image-classification explainable-vision-transformer semantic-segmentation


Explainable Isolation Forest

Explainable Isolation Forest (Isolation Forest with SHAP-based Interpretability)

XIF
Isolation Forest with SHAP
interpretable anomaly detection
explainable anomaly isolation

Liu, F. T., Ting, K. M., & Zhou, Z.-H. (Isolation Forest); Lundberg, S. M. & Lee, S.-I. (SHAP explainability layer)

Explainable Isolation Forest combines the Isolation Forest anomaly detection algorithm with post-hoc explainability tools — most commonly SHAP (SHapley Additive exPlanations) — to not only flag anomalous observations but also reveal which features drove each anomaly score. It bridges unsupervised anomaly detection with the interpretability demands of regulated and high-stakes domains.

Anomaly detection with post-hoc explainability

Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation forest. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), pp. 413–422. IEEE.

isolation-forest one-class-svm autoencoder-anomaly-detection explainable-random-forest gaussian-mixture-model explainable-gradient-boosting


Explainable K-Means

ExKMC
interpretable k-means
decision-tree k-means
explainable clustering

Dasgupta, S.; Moshkovitz, M.; Frost, N.; Rashtchian, C.

Explainable K-Means is a post-hoc and in-model interpretability approach to standard K-Means clustering that replaces or approximates cluster assignments with a small axis-aligned decision tree. Each leaf of the tree corresponds to one cluster, and every data point is assigned to a cluster by following a simple sequence of threshold rules on individual features — making cluster membership fully transparent and human-readable.

Explainable unsupervised clustering algorithm

Dasgupta, S., Frost, N., Moshkovitz, M., & Rashtchian, C. (2020). Explainability of k-Means Clustering. Proceedings of the 37th International Conference on Machine Learning (ICML), PMLR 119.

Moshkovitz, M., Dasgupta, S., Rashtchian, C., & Frost, N. (2020). Explainable k-Means and k-Medians Clustering. Proceedings of the 37th International Conference on Machine Learning (ICML), PMLR 119.

k-means-clustering decision-tree hierarchical-clustering dbscan gaussian-mixture-model random-forest


Explainable K-Nearest Neighbors

XKNN
Interpretable KNN
Explainable KNN
Transparent K-Nearest Neighbors

1967 (KNN); 2010s (explainability extensions)

Cover, T. & Hart, P. (KNN); XAI extensions by various authors

Explainable K-Nearest Neighbors (XKNN) augments the classic KNN classifier or regressor with structured post-hoc or built-in explanation mechanisms, exposing which retrieved neighbors, which features, and which distance contributions drive each individual prediction — making the model's reasoning transparent and auditable for human decision-makers.

Instance-based learning with explainability layer

Cover, T. & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27.

Papernot, N. & McDaniel, P. (2018). Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning. arXiv preprint arXiv:1803.04765.

k-nearest-neighbors lime shap decision-tree random-forest naive-bayes


Explainable LDA Topic Model

Explainable Latent Dirichlet Allocation Topic Model

Explainable LDA
Interpretable LDA
XAI-LDA
Transparent Topic Model

2003 (LDA); 2018–present (explainability extensions)

Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA seminal); explainability extensions by multiple authors

Explainable LDA combines Latent Dirichlet Allocation — the canonical probabilistic topic model introduced by Blei, Ng, and Jordan in 2003 — with post-hoc and intrinsic interpretability tools that make each discovered topic auditable, labeled, and trustworthy for human reviewers. It is widely used in NLP, social science text analysis, and computational humanities where transparency is required alongside discovery.

Probabilistic generative topic model with interpretability enhancements

Text corpora (bag-of-words or token sequences)

Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.

latent-dirichlet-allocation bert-topic-model non-negative-matrix-factorization neural-topic-model word2vec text-classification


Explainable LightGBM

Explainable LightGBM (LightGBM with SHAP-based Interpretability)

XAI-LightGBM
LightGBM with SHAP
Interpretable LightGBM
LightGBM explainability

Ke, G. et al. (LightGBM); Lundberg, S. M. & Lee, S.-I. (SHAP for tree models)

Explainable LightGBM combines Microsoft's LightGBM gradient boosting framework with SHAP (SHapley Additive exPlanations) to deliver both high predictive performance and rigorous, theoretically grounded feature-level explanations. It is widely adopted in applied research where predictive accuracy and interpretability are simultaneously required.

Gradient boosting with post-hoc explainability (SHAP)

Tabular (continuous, categorical, binary, ordinal)

Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30, 3146–3154.

xgboost gradient-boosting random-forest shap-analysis catboost decision-tree


Explainable LSTM

Explainable Long Short-Term Memory Network

XAI-LSTM
interpretable LSTM
LSTM with SHAP
transparent LSTM

Lundberg & Lee (SHAP); Ribeiro et al. (LIME); community synthesis

Explainable LSTM pairs a trained Long Short-Term Memory network with post-hoc interpretability techniques — chiefly SHAP, LIME, integrated gradients, or attention visualization — to reveal which time steps, tokens, or features drive each prediction. It bridges the accuracy of recurrent deep learning with the transparency demanded by high-stakes domains such as clinical decision support, fraud detection, and regulatory compliance.

Interpretable deep learning (post-hoc explainability)

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?": Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144.


Explainable Multilayer Perceptron

Explainable Multilayer Perceptron (MLP with Post-hoc Interpretability)

XMLP
Interpretable MLP
Explainable feedforward neural network
Transparent MLP

Lundberg & Lee (SHAP); Ribeiro et al. (LIME); broader XAI community

An Explainable Multilayer Perceptron (XMLP) is a standard feedforward neural network trained with backpropagation, augmented with post-hoc interpretability techniques — such as SHAP values, LIME, or integrated gradients — that attribute each prediction to individual input features. The combination retains the MLP's approximation power while satisfying transparency requirements common in regulated or high-stakes domains.

Supervised feedforward neural network with interpretability layer

Tabular, image, text (numeric input preferred)

Explainable artificial intelligence. Wikipedia.


Explainable Naive Bayes

XNB
interpretable Naive Bayes
transparent Naive Bayes
explainable probabilistic classifier

1950s (Naive Bayes); 2000s–2010s (explainability focus)

Zhang, H. (explainability framing); Naive Bayes: Good, I. J.

Explainable Naive Bayes extends the classic probabilistic Naive Bayes classifier with transparent, human-readable explanations of its predictions. By surfacing class priors, per-feature likelihoods, and log-odds contributions, it offers the interpretability demanded in high-stakes domains such as medicine, law, and education without sacrificing the simplicity and speed that make Naive Bayes a reliable baseline.

Probabilistic generative classifier with intrinsic explainability

Categorical, binary, or continuous features; labeled classification targets

Rish, I. (2001). An empirical study of the naive Bayes classifier. In IJCAI Workshop on Empirical Methods in AI (pp. 41–46).

naive-bayes logistic-regression decision-tree random-forest lime-explainability shap-values


Explainable Named Entity Recognition

XAI-NER
Interpretable NER
Transparent Named Entity Recognition
Explainable NER

Explainable Named Entity Recognition (XAI-NER) combines a standard NER model — typically a BERT-based or BiLSTM-CRF sequence labeler — with post-hoc or intrinsic explainability techniques such as LIME, SHAP, attention visualization, or gradient-based saliency to reveal why each token was assigned a particular entity label. This transparency is essential in high-stakes domains like clinical text, legal documents, and biomedical literature.

Text (token sequences with entity spans)

Danilevsky, M., Qian, K., Aharonov, R., Katsis, Y., Kawas, B., & Sen, P. (2020). A Survey of the State of Explainable AI for Natural Language Processing. Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL-IJCNLP), pp. 447–459.

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why Should I Trust You?": Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144.

named-entity-recognition bert-based-classification explainable-bert-based-classification explainable-text-summarization explainable-sentiment-analysis explainable-transformer


Explainable NMF Topic Model

Explainable Non-negative Matrix Factorization Topic Model

XAI-NMF
interpretable NMF topic model
explainable NMF
transparent NMF topic modeling

2001 (NMF); XAI integration ~2017–present

Lee, D. D. & Seung, H. S. (NMF); XAI layer attributed to community practice post-2016

An Explainable NMF Topic Model combines Non-negative Matrix Factorization — a parts-based decomposition of a document-term matrix — with explicit interpretability techniques such as coherence metrics, word contribution scores, and SHAP-style attribution to make discovered topics transparent and auditable by human readers.

Document-term matrices (bag-of-words or TF-IDF); text corpora

Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems, 13, 556–562.

Non-negative matrix factorization. Wikipedia.

nmf-topic-model lda-topic-model explainable-lda-topic-model topic-modeling explainable-bert-based-classification sentence-embeddings


Explainable Object Detection

Explainable Artificial Intelligence for Object Detection (XAI-OD)

XAI Object Detection
Interpretable Object Detection
Transparent Object Detection
Explainable OD

Selvaraju et al. (Grad-CAM); Ribeiro et al. (LIME); Lundberg & Lee (SHAP)

Explainable object detection combines a deep-learning object detector — such as YOLO, Faster R-CNN, or DETR — with post-hoc or built-in explainability methods (Grad-CAM, LIME, SHAP, D-RISE) that visualize why the model placed a bounding box at a particular location and assigned a particular class label, making its decisions auditable by humans.

Post-hoc explainability applied to object detection

Images (RGB; multi-scale; annotated bounding boxes)

Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 618–626.

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). 'Why Should I Trust You?': Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144.


Explainable One-Class SVM

Explainable One-Class Support Vector Machine

XOC-SVM
Interpretable One-Class SVM
SHAP-augmented OCSVM
Explainable Novelty Detection SVM

1999 (OCSVM); 2017–present (explainability integration)

Schölkopf, B. et al. (OCSVM); explainability layer via Lundberg & Lee (SHAP, 2017) and related works

Explainable One-Class SVM pairs the classic One-Class Support Vector Machine anomaly detector — which learns a tight boundary around normal data without requiring labeled anomalies — with post-hoc explainability methods such as SHAP or LIME to reveal which features drive each novelty or anomaly score, converting an opaque decision boundary into an auditable, feature-attributable signal.

Anomaly/novelty detection with post-hoc or intrinsic explainability

Continuous or mixed tabular data; unlabeled or predominantly normal-class data

Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., & Platt, J. (1999). Support vector method for novelty detection. Advances in Neural Information Processing Systems, 12, 582–588.

Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30.

one-class-svm isolation-forest local-outlier-factor autoencoder-anomaly-detection shap-explainability support-vector-machine


Explainable Question Answering

XQA
interpretable QA
transparent question answering
rationale-based QA

Community (DeYoung et al.; Rajpurkar et al.)

Explainable Question Answering (XQA) combines neural reading-comprehension models — typically BERT-family transformers — with interpretability methods such as rationale extraction, attention visualization, LIME, or SHAP to reveal why the model selected a particular answer span. The goal is not just accuracy but trustworthy, auditable reasoning that users and domain experts can inspect and verify.

Text (passages, questions, answer spans)

DeYoung, J., Jain, S., Rajani, N. F., Lehman, E., Xiong, C., Socher, R., & Wallace, B. C. (2020). ERASER: A Benchmark to Evaluate Rationalized NLP Models. In Proceedings of ACL 2020, pp. 4443–4458.

Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. In Proceedings of EMNLP 2016, pp. 2383–2392.

bert-based-classification roberta-based-classification explainable-bert-based-classification transformer sentence-embeddings explainable-transformer


Explainable Random Forest

Explainable Random Forest (Interpretable Ensemble with Feature Attribution)

XRF
interpretable random forest
transparent random forest
random forest with explainability

Breiman, L. (RF); Lundberg & Lee (SHAP attribution)

Explainable Random Forest (XRF) combines the predictive power of Breiman's Random Forest ensemble with systematic post-hoc attribution methods — principally SHAP values and mean-decrease-in-impurity importance — to make model decisions transparent and auditable. It delivers both high accuracy and human-interpretable feature contributions, satisfying demands from regulators, domain experts, and academic reviewers alike.

Interpretable ensemble (bagging + post-hoc attribution)

Tabular (continuous, categorical, mixed)

Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.

Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.

random-forest shap-values decision-tree gradient-boosting xgboost lime-explainability


Explainable Recurrent Neural Network

Explainable Recurrent Neural Network (XAI-augmented RNN)

Explainable RNN
Interpretable RNN
XAI-RNN
Transparent Recurrent Neural Network

Arrived via XAI literature (Arrieta et al., Lundberg & Lee, and attention-based RNN work)

An Explainable Recurrent Neural Network (XAI-RNN) pairs a standard RNN architecture with a post-hoc or intrinsic interpretability method — such as SHAP, LIME, integrated gradients, or attention visualization — to reveal which input time steps or tokens most influence the model's sequential predictions, without sacrificing predictive accuracy.

Interpretability framework applied to sequence models

Arrieta, A. B., Diaz-Rodriguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115.


Explainable Reinforcement Learning

XRL
interpretable reinforcement learning
transparent RL
explainable RL

Puiutta, E. & Veith, E. M. S. P. (survey); broader XAI community

Explainable Reinforcement Learning (XRL) augments standard reinforcement learning agents with methods that make their policies, decisions, and learned behaviors interpretable to humans. Rather than treating the policy as a black box, XRL produces post-hoc explanations or builds inherently transparent policies, enabling trust verification, debugging, and accountability in high-stakes automated decision-making.

Hybrid approach (RL + explainability methods)

Sequential decision data, environment state-action trajectories

Puiutta, E., & Veith, E. M. S. P. (2020). Explainable Reinforcement Learning: A Survey. In Machine Learning and Knowledge Extraction (CD-MAKE 2020), Lecture Notes in Computer Science, vol. 12279, pp. 77–95. Springer.


Explainable RoBERTa-based Text Classification with Post-hoc Interpretation

XAI-RoBERTa
Interpretable RoBERTa Classifier
RoBERTa with SHAP/LIME
Transparent RoBERTa NLP

Liu et al. (RoBERTa, 2019); Lundberg & Lee (SHAP, 2017); Ribeiro et al. (LIME, 2016)

Explainable RoBERTa-based classification fine-tunes a RoBERTa transformer model on labeled text data and then applies post-hoc interpretability methods — such as SHAP, LIME, or attention analysis — to reveal which tokens or features drove each prediction. This bridges state-of-the-art NLP performance with human-understandable reasoning, satisfying both accuracy and transparency requirements.

Pre-trained transformer classifier with post-hoc XAI

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv preprint arXiv:1907.11692.

Lundberg, S. M., & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems (NeurIPS), 30, 4765–4774.

roberta-based-classification bert-based-classification explainable-bert-based-classification sentence-embeddings transformer explainable-transformer


Explainable Semantic Segmentation

Explainable Semantic Segmentation (XAI-Integrated Pixel-Wise Scene Parsing)

XSS
interpretable semantic segmentation
explainable scene parsing
transparent pixel-wise classification

Combination: Long et al. (FCN) + Selvaraju et al. (Grad-CAM); formalized as a unified paradigm ~2019–2021

Explainable Semantic Segmentation (XSS) couples pixel-wise scene parsing — assigning a class label to every pixel in an image — with post-hoc or intrinsic explanation methods such as Grad-CAM, attention maps, or SHAP, so that the network's class decisions can be audited, visualized, and justified to domain experts in medical imaging, autonomous driving, and remote sensing.

Images (RGB, medical, satellite, video frames)

Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 618–626.

Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3431–3440.

grad-cam semantic-segmentation instance-segmentation attention-mechanism shap lime


Explainable Sentence Embeddings

Explainable Sentence Embeddings (Interpretable Dense Sentence Representations)

interpretable sentence representations
XAI sentence embeddings
probing sentence embeddings
explainable sentence vectors

Conneau et al.; Ribeiro et al. (probing + LIME frameworks)

Explainable sentence embeddings combine dense sentence representation learning with post-hoc or intrinsic interpretability tools — such as probing classifiers, LIME, SHAP, or attention attribution — to reveal what linguistic and semantic information is encoded in a sentence vector and why a downstream model makes a given prediction. The goal is to retain the representational power of modern encoders while making their behavior auditable.

Post-hoc interpretability applied to sentence encoders

Conneau, A., Kruszewski, G., Lample, G., Barrault, L., & Baroni, M. (2018). What you can cram into a single $\vec{v}$ector: Probing sentence embeddings for linguistic properties. In Proceedings of ACL 2018, pp. 2126–2136.

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why Should I Trust You?": Explaining the predictions of any classifier. In Proceedings of KDD 2016, pp. 1135–1144.

sentence-embeddings bert-based-classification explainable-bert-based-classification explainable-recurrent-neural-network explainable-transformer self-supervised-sentence-embeddings


Explainable Sentiment Analysis

Explainable Sentiment Analysis (XAI-augmented Opinion Mining)

XAI sentiment analysis
interpretable sentiment classification
transparent opinion mining
explainable opinion analysis

Multiple contributors (LIME: Ribeiro et al. 2016; SHAP: Lundberg & Lee 2017; attention-based XAI in NLP: numerous, 2018–2020)

Explainable sentiment analysis pairs a sentiment classification model — typically a fine-tuned transformer such as BERT or RoBERTa — with a post-hoc or intrinsic explanation method (SHAP, LIME, attention visualization, or integrated gradients) that reveals which words, phrases, or features drove each prediction. The goal is both high predictive accuracy and transparent, auditable rationales for every label.

Text (reviews, social media, survey responses)

Danilevsky, M., Qian, K., Aharonov, R., Katsis, Y., Kawas, B., & Sen, P. (2020). A Survey of the State of Explainable AI for Natural Language Processing. Proceedings of the 1st Conference of the Asia-Pacific Chapter of the ACL and the 10th IJCNLP, 447–459.

bert-based-classification roberta-based-classification explainable-bert-based-classification sentence-embeddings fine-tuned-sentiment-analysis topic-modeling


Explainable Stacking Ensemble

Explainable Stacking Ensemble (Interpretable Meta-Learning)

XAI-Stacking
interpretable stacking
transparent stacking ensemble
explainable stacked generalisation

1992 (stacking); 2010s–2020s (explainable extensions)

Wolpert, D. H. (stacking); XAI integration developed across the community

Explainable Stacking Ensemble combines the predictive power of stacked generalisation — training a meta-learner on the outputs of multiple diverse base models — with interpretability tools such as SHAP or LIME that reveal how each base model and each input feature contributed to the final prediction. It bridges the accuracy–transparency trade-off that makes pure stacking opaque in high-stakes settings.

Ensemble meta-learning with post-hoc or intrinsic interpretability

Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.

random-forest xgboost bagging-ensemble gradient-boosting shap-explainability model-stacking