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Resources related to causality

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Resources related to causality. This awesome list is different from other lists as it tries to compile major resources related to causality in one place under different categories.

**NOTE:** This awesome list is still new and under development. Please feel free to contribute, before it can become worth sharing.

*Table of contents generated with markdown-toc*

These list contain a more focused compilation of algorithms and data related to causality under more specific categories.

- Amazon Review Sales - Google drive - Paper
- Jobs Training - Train Test - Paper
- Twins
- Synthetic IHDP
- 2016 Atlantic Causal Inference competition
- News trearment effect measurement
- Cause effect pairs
- Movie recommendations - Missing not at random (MNAR) - Paper
- CHALEARN Fast Causation Coefficient Challenge - Kaggle
- Causal inference datasets in quantitative social sciences

- Omega: Causal, Higher-Order, Probabilistic Programming
- Pyro: A probabilistic programming language built on PyTorch that contains the do() operator
- Whittemore: Causal Programming in Clojure
- causaleffect: Functions for identification and transportation of causal effects
- pgmpy: Probabilistic Graphical Models in python, extended to causal queries
- pyagrum: a GRaphical Universal Modeler with causal examples from the Book of Why
- Counterfactual regression
- DoWhy - Microsoft Research
- Quantitative Social Science - Book
- Causal Inference using Bayesian Additive Regression Trees
- Non-parametrics for Causal Inference
- Causality by author of Causal Data Science Series (see blogs)
- InvariantCausalPrediction: Invariant Causal Prediction
- Causal Discovery Toolbox
- CausalImpact - causal inference in time series
- Daggity - Create causal graphs
- TETRAD
- ProbLog - Do-calculus
- Causalnex - A toolkit for causal reasoning with Bayesian Networks
- Causal Fusion - A web based app for drawing and making inference via do-calculus on causal diagrams
- DiCE - Generate Diverse Counterfactual Explanations for any machine learning model

- ICML 2016 Tutorial Causal Inference for Observational Studies
- KDD 2018 Causal Inference Tutorial
- Joris Mooij ML2 Causality
- Emre Kiciman - Observational Studies in Social Media (OSSM) at ICWSM 2017
- The Blessings of Multiple Causes: A Tutorial
- Susan Athey: Counterfactual Inference (NeurIPS 2018 Tutorial) - Slides
- Ferenc Huszár Causal Inference Practical from MLSS Africa 2019 - [Notebook Runthrough] [Video 1] [Video 2]
- Causality notes and implementation in Python using statsmodels and networkX
- Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data
- The Hitchhiker’s Guide to the tlverse or a Targeted Learning Practitioner’s Handbook
- Causal Inference for The Brave and True

- Causal Data Science Series
- Ferenc Huszár Series on Causal Modelling: various parts - 1, 2, 3, 4
- Diving deeper into causality Pearl, Kleinberg, Hill and untested assumptions
- Simpson's Paradox: An Anatomy
- Simpson’s paradox and causal inference with observational data
- Causation and Correlation - Talks about possible causes for observed correlations
- (Non-)Identification in Latent Confounder Models
- Causal Inference Animated Plots - Good explanation of various causal inference methods
- Explanation, prediction, and causality: Three sides of the same coin?
- A chill intro to causal inference via propensity scores
- All the DAGs from Hernan and Robins' Causal Inference Book by Sam Finlayson - Causal Inference Book Part I -- Glossary and Notes
- Causal Inference with Bayes Rule by Gradient Institute
- Causal Inference cheat sheet for data scientists
- Which causal inference book you should read
- Tweetorial on going from regression to estimating causal effects with machine learning

- Causal Inference Book
- Causal Inference in statistics: A primer
- Elements of Causal Inference - Foundations and Learning Algorithms (includes code examples in R and Jupyter notebooks)
- The Book of Why: The New Science of Cause and Effect
- Causal Inference Mixtape
- Elements of Causal Inference - Foundations and Learning Algorithms
- Actual Causality By Joseph Y. Halpern
- Causal Reasoning: Fundamentals and Machine Learning Applications by Emre Kiciman and Amit Sharma

- Causal Diagrams: Draw Your Assumptions Before Your Conclusions
- Causal Inference: prediction, explanation, and intervention
- Causal Inference Experiments Short Course
- ECON 305: Economics, Causality, and Analytics [github]
- Algorithmic Information Dynamics: A Computational Approach to Causality and Living Systems From Networks to Cells
- Four Lectures on Causality by Jonas Peters
- Julian Schuessler's Causal Graphs Seminar - Winner of 2019 American Statistics Association Causality in Statistics Education Award
- Ilya Shpitser's course on Causal Inference (Zip file) - Winner of 2017 American Statistics Association Causality in Statistics Education Award
- Arvid Sjölander's course on Causal Inference (Zip file) - Winner of 2016 American Statistics Association Causality in Statistics Education Award
- Onyebuchi A. Arah course on Causality in Statistics (Dropbox folder) - Winner of 2016 American Statistics Association Causality in Statistics Education Award
- Introduction to causal inference by Maya L. Petersen & Laura B. Balzer
- Introduction to Causal Inference by Brady Neal

- PyData LA 2018 Keynote: Judea Pearl - The New Science of Cause and Effect
- CACM Mar. 2019 - The Seven Tools of Causal Inference
- ACM Turing Award Lecture 2011 - Judea Pearl
- Leon Bottou - Learning representations using causal invariance
- Online Causal Inference Seminar

- Beyond Curve Fitting: Causation, Counterfactuals, and Imagination-based AI
- Causality Challenge #1: Causation and Prediction
- NIPS 2013 Workshop on Causality
- ChaLearn Fast Causation Coefficient Challenge