A curated collection of resources on causality ranging from datasets, learning resources, and tools. Maintained by Shubhanshu Mishra

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 - [R code] [Python code]
- 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
- The Effect: An Introduction to Research Design and Causality

- 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
- NeurIPS 2020 Workshop: Causal Discovery and Causality-Inspired Machine Learning

- 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