Resources to get started on Causality

Here is a list of resources to get started on Causality, Causal Inference and Counterfactual Reasoning.

Reading

If you have 10 minutes, read this blog post - it’s very easy to read, it’s recent (as of 18th July, 2018), and gives an overall perspective of why causal inference might be a good framework to try.

If you have an hour to spare, read The Art and Science of Cause and Effect by Judea Pearl. This is the transcript of a public lecture by Judea Pearl delivered November 1996, and it brilliantly motivates why we need causality, and gives an overview of what the theory allows us to do.

If you have 8-10 hours to spare, and want to get into some level of detail, read Cosma Shalizi’s lecture notes for the course on Advanced Data Analysis. The relevant topics are Graphical Causal Models, Identifying Causal Effects from Observations, Estimating Causal Effects from Observations and Discovering Causal Structure from Observations.

If you want to understand Causality in depth, read Elements of Causal Inference by Jonas Peters, Dominik Janzing, Bernhard Schölkopf.

If you have a lot of time to spare, then read Judea Pearl’s classic book Causality

Watching

I’ve divided the videos into two categories - lectures and talks. Typically, lectures are a detailed series of videos. Talks are shorter, concise and sometimes also explain some exciting research areas and results.

Lectures

Mini Lecture Series on Causality by Jonas Peters at Broad Institute.

Talks

Causes and Counterfactuals: Concepts, Principles and Tools in Tutorial in NIPS 2013 by Judea Pearl and Elias Bareinboim.

Towards Causal Machine Learning by Bernhard Schölkopf

Learning Causal Mechanisms in ICLR 2018 by Bernhard Schölkopf

More Resources

A nice introduction to the different schools of thought in causal analysis is here.

Please let me know if I have missed any other resources!

(Updated 3rd September, 2018)