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https://github.com/pedrosan/CausalModeling

Last synced: 20 days ago
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Host: GitHub
URL: https://github.com/pedrosan/CausalModeling
Owner: pedrosan
Created: 2017-02-21T16:03:40.000Z (over 7 years ago)
Default Branch: master
Last Pushed: 2017-06-29T21:52:36.000Z (over 7 years ago)
Last Synced: 2024-07-31T19:17:01.469Z (4 months ago)
Language: CSS
Size: 26.4 KB
Stars: 11
Watchers: 3
Forks: 4
Open Issues: 0
Metadata Files:
- Readme: README.md
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README

        ## Causality Modeling Resources

You can use the [editor on GitHub](https://github.com/pedrosan/CausalModeling/edit/master/README.md) to maintain and preview the content for your website in Markdown files. 


## MXM

- [main page](http://mensxmachina.org/en/)

- [R package](https://cran.r-project.org/web/packages/MXM/index.html)

- [Tsamardinos academic page](http://www2.ics.forth.gr/bil/index_main.php?l=e&c=535)



## Lectures

### Courses

- Coursera "Probabilistic Graphical Models" (Koller)

  - [1. Representation](https://www.coursera.org/learn/probabilistic-graphical-models/)

  - [2. Inference](https://www.coursera.org/learn/probabilistic-graphical-models-2-inference)

  - [3. Learning](https://www.coursera.org/learn/probabilistic-graphical-models-3-learning)

- [Univ. of Pittsburgh CCD _Short Summer Course (2016)_](http://www.ccd.pitt.edu/2016-short-course-datathon/) 

  - [Videos (on YouTube)](https://youtu.be/9yEYZURoE3Y)

  - [Additional material on hackpad](https://hackpad.com/2016-Causal-Discovery-Course-RSf6boNbJVH)

  - Tetrad 5.3.0 JNLP version [[download](http://www.phil.cmu.edu/tetrad/jnlp/tetrad.jnlp.5.3.0.jnlp)]

  - Tetrad 5.3.0 jar file [[download](http://www.phil.cmu.edu/projects/tetrad_download/maven/edu/cmu/tetrad-gui/5.3.0-SNAPSHOT/tetrad-gui-5.3.0-20160607.191423-7-launch.jar)]

  - datasets [[download](http://www.phil.cmu.edu/projects/tetrad_download/download/workshops/CCD/2016/Datasets/)]

- [Univ. of Pittsburgh CCD _Short Summer Course (2015)_](http://www.ccd.pitt.edu/training/summer-short-course-2015/)

  - Tetrad 5.2.1.0 [[download](http://www.phil.cmu.edu/projects/tetrad_download/download/tetrad-5.2.1-0.jar)]

- [CMU Causal and Statistical Reasoning](http://oli.cmu.edu/courses/future/causal-statistical-reasoning/)

- [NIPS 2013 Workshop on Causality](http://clopinet.com/isabelle/Projects/NIPS2013/) 

 

 

### Videos

- [PGM Koller videos playlist](https://www.youtube.com/playlist?list=PL50E6E80E8525B59C)

### Lectures Notes

- [Stanford CS228 Lecture Notes](https://ermongroup.github.io/cs228-notes/)

  - [Introduction](https://ermongroup.github.io/cs228-notes/preliminaries/introduction/)

  - [Bayesian Networks](https://ermongroup.github.io/cs228-notes/representation/directed/)

- [NYU Sontag PGM course](http://cs.nyu.edu/~dsontag/courses/pgm13/)

- [Elwert _Causal Inference with DAGs_](http://www.ssc.wisc.edu/~felwert/causality/?page_id=66)

- [UC Biostat, _"Introduction to Causal Inference"_](http://www.ucbbiostat.com/lectures)

  - [Readings](http://www.ucbbiostat.com/readings)



## Reading Material

- [Judea Pearl home](http://bayes.cs.ucla.edu/home.htm)

  - ["Causality" book page](http://bayes.cs.ucla.edu/BOOK-2K/)

- [DAG (wikipedia)](https://en.wikipedia.org/wiki/Directed_acyclic_graph)

- [UCLA Department of Statistics publications (eScholarship)](https://escholarship.org/uc/uclastat_papers)

- [Uncertainty in Artificial Intelligence (Decision Science Labs @UPitt)](https://dslpitt.org/uai/home.jsp?mmnu=0&smnu=0)

### Blogs

- [Adam Kelleher blog series](https://medium.com/@akelleh/causal-data-science-721ed63a4027)

- [StitchFix: _Making Causal Impact Analysis Easy_](http://multithreaded.stitchfix.com/blog/2016/01/13/market-watch/)

- [Thomas Huijskens: _The fundamental problem of causal analysis_](https://thuijskens.github.io/2016/08/25/causal-modelling/)

- [Yanir Seroussi: _Why you should stop worrying about deep learning and deepen your understanding of causality instead_](https://yanirseroussi.com/2016/02/14/why-you-should-stop-worrying-about-deep-learning-and-deepen-your-understanding-of-causality-instead/)

- [_Probabilistic Graphical Models: Bayesian Networks Example With R, Python, SAMIAM_](http://plus8888.blogspot.com/2016/12/probabilistic-graphical-models-bayesian.html)

### Books

- Pearl: _"Causality"_ / [@UCLA](http://bayes.cs.ucla.edu/BOOK-2K/) / [@Amazon](https://www.amazon.com/dp/052189560X/)

- Koller & Friedman: _"Probabilistic Graphical Models"_ / [@MIT Press](https://mitpress.mit.edu/books/probabilistic-graphical-models) / [@Amazon](https://www.amazon.com/Probabilistic-Graphical-Models-Principles-Computation/dp/0262013193)

- Shalizi: _"Advanced Data Analysis from an Elementary Point of View"_ / [@CMU](http://www.stat.cmu.edu/~cshalizi/ADAfaEPoV/)

- van der Laan & Rose: _"Targeted Learning: Causal Inference for Observational and Experimental Data"_ / [website](http://www.targetedlearningbook.com/) / [@Amazon](https://www.amazon.com/Targeted-Learning-Observational-Experimental-Statistics/dp/1441997814) / [@Springer](http://www.springer.com/us/book/9781441997814)



## Research Groups

- [Probabilistic Graphical Models @Stanford](http://pgm.stanford.edu/)

- [Vanderbilt Discovery Systems Laboratory](http://www.dsl-lab.org/)

- [Peter Spirtes (@CMU Philosophy)](http://www.cmu.edu/dietrich/philosophy/people/faculty/spirtes.html)

- [University of Pittsburgh Center for Causal Discovery (CCD)](http://www.ccd.pitt.edu/data-science/)

- [James Robins (@Harvard SPH)](https://www.hsph.harvard.edu/james-robins/)

- [Tyler Vanderweele (@Harvard SPH)](https://www.hsph.harvard.edu/tyler-vanderweele/)

- [Jin Tian (@IAState CS)](http://web.cs.iastate.edu/~jtian/) ([`BNLearner` s/w](http://web.cs.iastate.edu/~jtian/Software/BNLearner/BNLearner.htm) )

- [Sander Greenland (@UCLA)](http://ph.ucla.edu/faculty/greenland)

- [Ilya Shpitser (@JHU CS)](https://www.cs.jhu.edu/faculty/ilya-shpitser-3/)

- [Project X Research - Direct Graphical Models](http://research.project-10.de/dgm/)

- [ETHZ causality resources](http://www.causality.inf.ethz.ch/resources.php)

- [Cosma Shalizi (@CMU Stats)](http://www.stat.cmu.edu/~cshalizi/)



## Software

- `bnlearn` R package: [bnlearn](http://www.bnlearn.com/) ([@CRAN](https://cran.r-project.org/web/packages/bnlearn/index.html))

- Adam Kelleher `causality` python package: [pypi](https://pypi.python.org/pypi/causality/0.0.3) and [GitHub](https://github.com/akelleh/causality)

- [Google's `CausalImpact` R package](https://google.github.io/CausalImpact/CausalImpact.html) / [on GitHub](https://google.github.io/CausalImpact/)

- [TETRAD](http://www.phil.cmu.edu/tetrad/) / [on GitHub](https://github.com/cmu-phil/tetrad)

  - [University of Pittsburgh Center for Causal Discovery (CCD)](http://www.ccd.pitt.edu/data-science/)

  - [CCD software documentation](https://bd2kccd.github.io/docs/)

- [ETHZ causality resources](http://www.causality.inf.ethz.ch/resources.php)

- Other python packages:

  - `pycausal`: [pypi](https://pypi.python.org/pypi/pycausal/) and [GitHub](https://github.com/triptoes1/pycausal/)

  - `causalmodels`: [pypi](https://pypi.python.org/pypi/causalmodels/) and [GitHub](https://github.com/roronya/causalmodels)

  - `pgmpy`: [web page](http://pgmpy.org/index.html)

- [UnBBayes (open source s/w for modeling, learning and reasoning upon probabilistic networks](http://unbbayes.sourceforge.net/)

- [SamIam (@UCLA)](http://reasoning.cs.ucla.edu/samiam/)

- [`BNLearner`](http://web.cs.iastate.edu/~jtian/Software/BNLearner/BNLearner.htm)

- [BDGAL, Bayesian DAG Learning](http://www.cs.ubc.ca/~murphyk/Software/BDAGL/)

- [`DAGitty`: A Graphical Tool for Analyzing Causal Diagrams](http://dagitty.net/)

  - [online browser based version](http://dagitty.net/dags.html)

  - [LOCAL version](file://chnas02/DSaT/Decision%20Sciences%20Team/Data%20Scientists/Projects/CausalModeling/software/DAGitty/dags.html)



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