.. _documentation-main:

Documentation
=============

Sponsor
-------

.. _doc-sponsor:

.. include:: sponsor.rst

Blog Posts
-----------------

.. _doc-blog-posts:

.. list-table::
   :header-rows: 1
   :widths: 70 30

   * - Topic
     - Medium

   * - **Causal Discovery – Overview and Starters Guide** – Foundations of causal modelling.
     - `Read <https://medium.com/data-science-collective/the-starters-guide-to-causal-structure-learning-with-bayesian-methods-in-python-e3b90f49c99c>`_

   * - **Structure Learning** – Learn structure from data or expert knowledge.
     - `Read <https://medium.com/data-science-collective/the-complete-starter-guide-for-causal-discovery-using-bayesian-modeling-8853eb860d02>`_

   * - **Causal Predictions** – Move beyond prediction to intervention.
     - `Read <https://medium.com/data-science-collective/why-prediction-isnt-enough-using-bayesian-models-to-change-the-outcome-5c9cf9f65a75>`_

   * - **Parameter Learning** – Estimate CPDs from observed data.
     - `Read <https://medium.com/data-science-collective/human-machine-collaboration-with-bayesian-modeling-learn-to-combine-knowledge-with-data-1ee9bcd67745>`_

   * - **Causal Inference** – Interventional & counterfactual reasoning.
     - `Read <https://medium.com/data-science-collective/chat-with-your-dataset-using-bayesian-inferences-1afdbfd4bada>`_

   * - **Generate Synthetic Data** – Forward sampling & data generation.
     - `Read <https://medium.com/data-science-collective/synthetic-data-the-essentials-of-data-generation-using-bayesian-sampling-6d072e97e09d>`_

   * - **Discretize Data** – Convert continuous variables for BN modelling.
     - —

   * - **Comparisons** – Evaluate Bayesian causal libraries.
     - `Read <https://medium.com/data-science-collective/six-causal-libraries-compared-which-bayesian-approach-finds-hidden-causes-in-your-data-9fa66fd02825>`_


GitHub Repository
-----------------

.. _doc-github:

.. note::
	`Source code of bnlearn can be found at GitHub <https://github.com/erdogant/bnlearn/>`_

Google Colab Notebooks
----------------------

.. _doc-colab:

.. note::
	* `General Functionalities <https://colab.research.google.com/github/erdogant/bnlearn/blob/master/notebooks/bnlearn.ipynb>`_
	* `Inferences on the Salary Dataset <https://colab.research.google.com/github/erdogant/bnlearn/blob/master/notebooks/inferences_on_salary_dataset.ipynb>`_
	* `Knowledge-Driven Approach <https://colab.research.google.com/github/erdogant/bnlearn/blob/master/notebooks/sprinkler_knowlegde_driven.ipynb>`_

Citing bnlearn
--------------

.. _doc-citing:

The BibTeX citation can be found in the right side menu at the `GitHub page <https://github.com/erdogant/bnlearn/>`_.

References
----------

.. _doc-references:

* `Probabilistic Graphical Models using pgmpy <https://conference.scipy.org/proceedings/scipy2015/pdfs/ankur_ankan.pdf>`_
* `Causality, Pearl, 2009, 2nd Edition <http://ftp.cs.ucla.edu/pub/stat_ser/r350.pdf>`_
* `If Correlation Doesn't Imply Causation, Then What Does? by Michael Nielsen <http://www.michaelnielsen.org/ddi/if-correlation-doesnt-imply-causation-then-what-does/>`_
* `Lecture Notes from Jonas Peters <http://www.math.ku.dk/~peters/jonas_files/scriptChapter1-4.pdf>`_
* `Elements of Causal Inference <http://www.math.ku.dk/~peters/jonas_files/bookDRAFT5-online-2017-02-27.pdf>`_
* `Causality Slides <http://mlss.tuebingen.mpg.de/2017/speaker_slides/Causality.pdf>`_

Related Packages
----------------

.. _doc-related-packages:

* `Causal Graphical Models <https://github.com/ijmbarr/causalgraphicalmodels>`_
* `Causality <https://github.com/akelleh/causality>`_
* `Causal Inference <https://github.com/laurencium/causalinference>`_

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