awesome-causality
Resources related to causality
https://github.com/napsternxg/awesome-causality
Last synced: 17 days ago
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Blogs, and Articles
- Causal Data Science Series
- Ferenc Huszár Series on Causal Modelling: various parts - [1](https://www.inference.vc/untitled/), [2](https://www.inference.vc/blessings-of-multiple-causes-causal-inference-when-you-cant-measure-confounders/), [3](https://www.inference.vc/causal-inference-2-illustrating-interventions-in-a-toy-example/), [4](https://www.inference.vc/causal-inference-3-counterfactuals/)
- 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](https://sgfin.github.io/2019/06/19/Causal-Inference-Book-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 in AI Education: A Primer - Accompanying Tool [Learn.CI](https://learn.ci/)
- The Effect: An Introduction to Research Design and Causality
- What is Causal Inference and How Does It Work?
- (Non-)Identification in Latent Confounder Models
- Explanation, prediction, and causality: Three sides of the same coin?
- A chill intro to causal inference via propensity scores
- Causal Inference cheat sheet for data scientists
- Tweetorial on going from regression to estimating causal effects with machine learning
- Simpson's Paradox: An Anatomy
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Books
- 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](https://github.com/scunning1975/mixtape_learnr)] [[Python code](https://github.com/tomcaputo/mixtape_learnr/tree/main/Python)]
- 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
- Bayesuvius: a visual dictionary of Bayesian Networks and Causal Inference - [github](https://github.com/rrtucci/Bayesuvius/)
- Causal Inference for Data Science - [github](https://github.com/aleixrvr/CausalInference4DataScience)
- Causal Machine Learning - [github](https://github.com/robertness)
- Causal Inference for The Brave and True
- Elements of Causal Inference - Foundations and Learning Algorithms
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Courses
- Causal Inference: prediction, explanation, and intervention
- ECON 305: Economics, Causality, and Analytics - K/introcausality)
- 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
- 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 Brady Neal
- Four Lectures on Causality by Jonas Peters
- Julian Schuessler's Causal Graphs Seminar - Winner of 2019 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
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Other Awesome lists
- Twins
- Synthetic IHDP
- 2016 Atlantic Causal Inference competition
- News trearment effect measurement
- Movie recommendations - Missing not at random (MNAR) - [Paper](http://proceedings.mlr.press/v48/schnabel16.html)
- CHALEARN Fast Causation Coefficient Challenge - [Kaggle](https://www.kaggle.com/c/cause-effect-pairs#description)
- Omega: Causal, Higher-Order, Probabilistic Programming
- causaleffect: Functions for identification and transportation of causal effects - icons@latest/icons/r.svg" />
- pyagrum: a GRaphical Universal Modeler with causal examples from the Book of Why - icons@latest/icons/python.svg" />
- InvariantCausalPrediction: Invariant Causal Prediction - icons@latest/icons/r.svg" />
- Daggity - Create causal graphs - icons@latest/icons/r.svg" />
- TETRAD - icons@latest/icons/java.svg" />
- ProbLog - Do-calculus - icons@latest/icons/python.svg" />
- Causal Fusion - A web based app for drawing and making inference via do-calculus on causal diagrams
- CCD Causal Software suite
- awesome-causality-algorithms
- awesome-causality-data
- Amazon Review Sales - [Google drive](https://drive.google.com/drive/u/1/folders/1Ff_GdfjhrDFbZiRW0z81lGJW-cUrYmo1) - [Paper](https://arxiv.org/abs/1808.03333)
- Jobs Training - [Train](http://www.fredjo.com/files/jobs_DW_bin.train.npz) [Test](http://www.fredjo.com/files/jobs_DW_bin.test.npz) - [Paper](http://proceedings.mlr.press/v70/shalit17a.html)
- Jobs Training - [Train](http://www.fredjo.com/files/jobs_DW_bin.train.npz) [Test](http://www.fredjo.com/files/jobs_DW_bin.test.npz) - [Paper](http://proceedings.mlr.press/v70/shalit17a.html)
- Cause effect pairs
- Causal inference datasets in quantitative social sciences
- Omega: Causal, Higher-Order, Probabilistic Programming
- Whittemore: Causal Programming in Clojure
- Counterfactual regression - icons@latest/icons/python.svg" />
- Quantitative Social Science - [Book](https://github.com/kosukeimai/qss) <img height="16" width="16" color="blue" src="https://unpkg.com/simple-icons@latest/icons/r.svg" />
- Causal Inference using Bayesian Additive Regression Trees - icons@latest/icons/r.svg" />
- Non-parametrics for Causal Inference - icons@latest/icons/r.svg" />
- Causality by author of Causal Data Science Series (see blogs) - icons@latest/icons/python.svg" />
- Causal Discovery Toolbox - icons@latest/icons/python.svg" />
- CausalImpact - causal inference in time series - icons@latest/icons/r.svg" />
- Causalnex - A toolkit for causal reasoning with Bayesian Networks
- DiCE - Generate Diverse Counterfactual Explanations for any machine learning model
- The TETRAD Project - searching for causal explanations of data
- Causal ML: A Python Package for Uplift Modeling and Causal Inference with ML
- Causality Lab - research code of novel causal discovery algorithms developed at Intel Labs
- DoWhy - Microsoft Research - icons@latest/icons/python.svg" />
- News trearment effect measurement
- causaleffect: Functions for identification and transportation of causal effects - icons@latest/icons/r.svg" />
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Tutorials
- 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
- Susan Athey: Counterfactual Inference (NeurIPS 2018 Tutorial) - [Slides](https://web.archive.org/web/20181214003957/https://media.neurips.cc/Conferences/NIPS2018/Slides/Counterfactual_Inference.pdf)
- Ferenc Huszár Causal Inference Practical from MLSS Africa 2019 - [\[Notebook Runthrough\]](https://www.youtube.com/watch?v=evmGGusk6gg) [\[Video 1\]](https://www.youtube.com/watch?v=HOgx_SBBzn0) [\[Video 2\]](https://www.youtube.com/watch?v=_RtxTpOb8e4)
- 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
- The Blessings of Multiple Causes: A Tutorial
- Causality notes and implementation in Python using statsmodels and networkX
- Causal Inference for The Brave and True
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Videos
- 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
- Okke van der Wal - Personalization at Uber scale via causal-driven machine learning | PDAMS 2023
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Workshops
- 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
- Causal Inference Reading list
- Causal inference paper reading list
- NIPS 2013 Workshop on Causality
- American Statistics Association Causality in Statistics Education Award
- NIPS 2013 Workshop on Causality
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