Causality Modeling Resources

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MXM

• main page
• R package
• Tsamardinos academic page

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Lectures

Courses

• Coursera "Probabilistic Graphical Models" (Koller)
  • 1. Representation
  • 2. Inference
  • 3. Learning
• Univ. of Pittsburgh CCD Short Summer Course (2016)
  • Videos (on YouTube)
  • Additional material on hackpad
  • Tetrad 5.3.0 JNLP version [download]
  • Tetrad 5.3.0 jar file [download]
  • datasets [download]
• Univ. of Pittsburgh CCD Short Summer Course (2015)
  • Tetrad 5.2.1.0 [download]
• CMU Causal and Statistical Reasoning
• NIPS 2013 Workshop on Causality

Videos

• PGM Koller videos playlist

Lectures Notes

• Stanford CS228 Lecture Notes
  • Introduction
  • Bayesian Networks
• NYU Sontag PGM course
• Elwert Causal Inference with DAGs
• UC Biostat, "Introduction to Causal Inference"
  • Readings

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Reading Material

• Judea Pearl home
  • "Causality" book page
• DAG (wikipedia)
• UCLA Department of Statistics publications (eScholarship)
• Uncertainty in Artificial Intelligence (Decision Science Labs @UPitt)

Blogs

• Adam Kelleher blog series
• StitchFix: Making Causal Impact Analysis Easy
• Thomas Huijskens: The fundamental problem of causal analysis
• Yanir Seroussi: 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

Books

• Pearl: "Causality" / @UCLA / @Amazon
• Koller & Friedman: "Probabilistic Graphical Models" / @MIT Press / @Amazon
• Shalizi: "Advanced Data Analysis from an Elementary Point of View" / @CMU
• van der Laan & Rose: "Targeted Learning: Causal Inference for Observational and Experimental Data" / website / @Amazon / @Springer

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Research Groups

• Probabilistic Graphical Models @Stanford
• Vanderbilt Discovery Systems Laboratory
• Peter Spirtes (@CMU Philosophy)
• University of Pittsburgh Center for Causal Discovery (CCD)
• James Robins (@Harvard SPH)
• Tyler Vanderweele (@Harvard SPH)
• Jin Tian (@IAState CS) (BNLearner s/w )
• Sander Greenland (@UCLA)
• Ilya Shpitser (@JHU CS)
• Project X Research - Direct Graphical Models
• ETHZ causality resources
• Cosma Shalizi (@CMU Stats)

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Software

• bnlearn R package: bnlearn (@CRAN)
• Adam Kelleher causality python package: pypi and GitHub
• Google's CausalImpact R package / on GitHub
• TETRAD / on GitHub
  • University of Pittsburgh Center for Causal Discovery (CCD)
  • CCD software documentation
• ETHZ causality resources
• Other python packages:
  • pycausal: pypi and GitHub
  • causalmodels: pypi and GitHub
  • pgmpy: web page
• UnBBayes (open source s/w for modeling, learning and reasoning upon probabilistic networks
• SamIam (@UCLA)
• BNLearner
• BDGAL, Bayesian DAG Learning
• DAGitty: A Graphical Tool for Analyzing Causal Diagrams
  • online browser based version
  • LOCAL version

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