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https://github.com/rmoraffa/awesome-causalinference

A curated list of Causal Inference tutorial, software, twitter, etc.
https://github.com/rmoraffa/awesome-causalinference

List: awesome-causalinference

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A curated list of Causal Inference tutorial, software, twitter, etc.

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# Awesome Causal Inference [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)



A curated list of Causal Inference tutorial, software, twitter, etc.



### Table of contents



* [Motivation](#motivation)

* [MOOC's](#moocs)

* [Blog Posts](#blog-Posts)

* [Books](#books)

* [Technical and white papers](#technical-and-white-papers)

* [Confrences](#confrences)

* [People](#people)

* [Toolboxes - Environment](#toolboxes---environment)

* [Comics](#comics)

* [Tutorials](#tutorials)

* [Contributing](#contributing)



## Motivation



"Correlation does not imply causation". Every data scientist you meet probably mumbles this while sleeping. So what implies causation?

Cause, as verb and a noun was studied, discusses and argued about from early days. In the context of statistics we try to quantify how one event effect on another event given set of assumptions. This corresponds to many research questions we face daily, such as - which treatment works better, how does minimum wage influence unemployment, how ads influence the users in our app, etc. This repository aggregates causal inference related resources.



## MOOC's



* [Coursera's Causality Crash Course](https://www.coursera.org/learn/crash-course-in-causality)

* Duke Causal Inference with R course - [Introduction](https://online.duke.edu/course/causal-inference-with-r-introduction/), [Experiments](https://online.duke.edu/course/causal-inference-with-r-experiments/), [Regression](https://online.duke.edu/course/causal-inference-with-r-regression/)



## Blog Posts



* Ferenc Huszár serier about cxausality - [part 1](https://www.inference.vc/untitled/), [part 2](https://www.inference.vc/causal-inference-2-illustrating-interventions-in-a-toy-example/), [part 3](https://www.inference.vc/causal-inference-3-counterfactuals/)

* [A Simple Guide to Doubly Robust Estimation](http://www.amitsharma.in/post/doubly-robust-estimation-a-simple-guide/)

* [Mediation Modeling at Uber: Understanding Why Product Changes Work (and Don’t Work)](https://eng.uber.com/mediation-modeling/)

* [Using Causal Inference to Improve the Uber User Experience](https://eng.uber.com/causal-inference-at-uber/)

* Lyft Engineering Blog: Experimentation in a Ridesharing Marketplace - [part1](https://eng.lyft.com/experimentation-in-a-ridesharing-marketplace-b39db027a66e), [part2](https://eng.lyft.com/https-medium-com-adamgreenhall-simulating-a-ridesharing-marketplace-36007a8a31f2), [part3](https://eng.lyft.com/experimentation-in-a-ridesharing-marketplace-f75a9c4fcf01)

* [All the DAGs from Hernan and Robins' Causal Inference Book](https://sgfin.github.io/2019/06/19/Causal-Inference-Book-All-DAGs/)

* [Introducing the do-sampler for causal inference](https://medium.com/@akelleh/introducing-the-do-sampler-for-causal-inference-a3296ea9e78d)

* [Awesome Causaliity Algorithms](https://github.com/rguo12/awesome-causality-algorithms)



## Books

* [Causal Inference - Hernán MA, Robins JM (2019)](https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/)

* [Elements of Causal Inference - Jonas Peters, Dominik Janzing and Bernhard Schölkopf](https://mitpress.mit.edu/books/elements-causal-inference)

* [Causality - Judea Pearl](https://www.amazon.com/Causality-Judea-Pearl-ebook/dp/B00AKE1VYK)

* [The Book of Why - Judea Peral](https://www.amazon.com/Book-Why-Science-Cause-Effect-ebook/dp/B075DCKP7V)

* [Causal Inference in Statistics: A Primer - Judea Pearl](https://www.amazon.com/Causal-Inference-Statistics-Judea-Pearl-ebook/dp/B01B3P6NJM)



## Technical and white papers

* [Causal inference in statistics: An overview - Judea Pear](http://ftp.cs.ucla.edu/pub/stat_ser/r350.pdf)



## Confrences

* [WHY19 - Beyond Curve Fitting: Causation, Counterfactuals, and Imagination-based AI](https://why19.causalai.net/)

* [EuroCim 2018 - European Causal Inference Meeting](http://eurocim2018.arcolab.org/)

* [ACIC 2019 - Atlantic Causal Inference Conference](https://www.mcgill.ca/epi-biostat-occh/news-events/atlantic-causal-inference-conference-2019)

* [ACIC 2018 - Atlantic Causal Inference Conference](https://www.cmu.edu/acic2018/)



## People



| Name | homepage | Twitter |

| -------- | ---- | ---- |

| Judea Pearl |[http://bayes.cs.ucla.edu/jp_home.html](http://bayes.cs.ucla.edu/jp_home.html) | [yudapearl](https://twitter.com/yudapearl) |

| Suchi Saria | [https://suchisaria.jhu.edu/](https://suchisaria.jhu.edu/) | [suchisaria](https://twitter.com/suchisaria) |

| Uri Shalit | [https://web.iem.technion.ac.il/en/people/urishalit.html](https://web.iem.technion.ac.il/en/people/urishalit.html) | [ShalitUri](https://twitter.com/ShalitUri)|

| Daniel Nevo | [https://danielnevo.wordpress.com/](https://danielnevo.wordpress.com/)|[DanielNevo](https://twitter.com/DanielNevo)|

| Ziad Obermayer | [http://sph.berkeley.edu/ziad-obermeyer-md](http://sph.berkeley.edu/ziad-obermeyer-md) |[oziadias](https://twitter.com/oziadias)|

| Miguel Hernan | [https://www.hsph.harvard.edu/miguel-hernan/](https://www.hsph.harvard.edu/miguel-hernan/) |[_MiguelHernan](https://twitter.com/_MiguelHernan)|

| Jamie Robins | [https://www.hsph.harvard.edu/james-robins/](https://www.hsph.harvard.edu/james-robins/) | |

| Amit Sharma | [http://www.amitsharma.in/](http://www.amitsharma.in/) |[amt_shrma](https://twitter.com/amt_shrma)|

| Emre Kıcıman | [http://kiciman.org/](http://kiciman.org/) |[emrek](https://twitter.com/emrek)|

| Elias Bareinboim |[https://causalai.net/](https://causalai.net/) |[eliasbareinboim](https://twitter.com/eliasbareinboim)|

| Alex Dimakis | [https://users.ece.utexas.edu/~dimakis/](https://users.ece.utexas.edu/~dimakis/)| [AlexGDimakis](https://twitter.com/AlexGDimakis)|

| Csaba Szepesvari | [https://sites.ualberta.ca/~szepesva/](https://sites.ualberta.ca/~szepesva/) |[CsabaSzepesvari](https://twitter.com/CsabaSzepesvari)|

| Ellie Murray | [https://scholar.harvard.edu/eleanormurray/home](https://scholar.harvard.edu/eleanormurray/home) | [EpiEllie]((https://twitter.com/EpiEllie))|

| Luke Keele | [http://lukekeele.com/](http://lukekeele.com/)| |

| Ilya Shpitser | [https://www.cs.jhu.edu/~ilyas/](https://www.cs.jhu.edu/~ilyas/) | |

| David Blie | [http://www.cs.columbia.edu/~blei/](http://www.cs.columbia.edu/~blei/) | |

| Alex D'Amour | [http://www.alexdamour.com/](http://www.alexdamour.com/) | [alexdamour](https://twitter.com/alexdamour)|

| Avi Feller | [https://gsppi.berkeley.edu/avi-feller/](https://gsppi.berkeley.edu/avi-feller/) | [avifeller](https://twitter.com/avifeller)|

| Bernhard Schölkopf | [http://is.tuebingen.mpg.de/person/bs](http://is.tuebingen.mpg.de/person/bs) | [bschoelkopf](https://twitter.com/bschoelkopf)|

| Sharon-Lise Normand | [https://hcp.hms.harvard.edu/people/sharon-lise-normand](https://hcp.hms.harvard.edu/people/sharon-lise-normand) | | 

| Guido Imbens | [https://imbens.people.stanford.edu/](https://imbens.people.stanford.edu/) | | 



## Toolboxes - Environment



* [DoWhy](https://github.com/Microsoft/dowhy) - Microsoft causal inference Python package

* [Causallib](https://github.com/IBM/causallib) - IBM causal inference Python package

* [dagitty](http://dagitty.net/) - browser-based environment for creating, editing, and analyzing causal models.

* [CausalImpact](https://google.github.io/CausalImpact/) - R package for causal inference in time series

* [MatchIt](https://gking.harvard.edu/matchit) - R package for nonparametric preprocessing for parametric causal inference

* [zEpid](https://github.com/pzivich/zEpid) - Epidemiology analysis package including effect measure plots and causal inference tools.



## Comics

* [Correlation](https://www.xkcd.com/552/)



## Tutorials

* [Amit Sharma and Emre Kıcıman KDD 2018 Causal Inference Tutorial](https://causalinference.gitlab.io/kdd-tutorial/)

* [Causal forests - A tutorial in high-dimensional causal inference by Ian Lundberg](https://scholar.princeton.edu/sites/default/files/bstewart/files/lundberg_methods_tutorial_reading_group_version.pdf)



## Contributing



Contributions are very welcome, please follow format and create a pull request.