Sponsor
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.. include:: sponsor.rst


Medium Blog
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.. note::
	* `Guide in detecting causal relationships using Bayesian Structure Learning in Python. <https://towardsdatascience.com/a-step-by-step-guide-in-detecting-causal-relationships-using-bayesian-structure-learning-in-python-c20c6b31cee5>`_
	* `Guide in designing knowledge-driven models using Bayesian theorem. <https://towardsdatascience.com/a-step-by-step-guide-in-detecting-causal-relationships-using-bayesian-structure-learning-in-python-c20c6b31cee5>`_
	* `A Comparative Analysis of Libraries to Reveal Hidden Causality in Your Dataset. <https://towardsdatascience.com/the-power-of-bayesian-causal-inference-a-comparative-analysis-of-libraries-to-reveal-hidden-d91e8306e25e>`_


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


Colab Notebook
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.. note::

	* `General functionalities <https://colab.research.google.com/github/erdogant/bnlearn/blob/master/notebooks/bnlearn.ipynb>`_
	* `Inferences on the salary data sets <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
#########

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



References
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* `Probabilistic Graphical Models using pgmpy <https://conference.scipy.org/proceedings/scipy2015/pdfs/ankur_ankan.pdf>`_
* `Causality, Pearl, 2009, 2nd Editing <http://ftp.cs.ucla.edu/pub/stat_ser/r350.pdf>`_
* `If correlation doesn't imply causation, then what does? from 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
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* `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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