is a platform to help you get started with applying and researching causal inference.

There is a weekly reading group you can join in via video call.

Check out the resource tab, which has useful links, including a reading list for starters.

Causal Inference: Beginner's guide

Getting started in causal inference is currently not easy, as reserach is relatively new and moving fast. Here is a list of books that can help you get the idea of causal inference, what it's philosophy is and how to apply it. This list is based on this more extensive reading list.

Beginner: Getting started

  1. If you want a 3 chapter long brief introduction by Judea Pearl himself, you should read Pearl's Causal Inference in Statistics: A Primer
  2. If you want a quick blog post introduction, I recommend Ferenc's entry here.
  3. If you are not interested in the math at the moment, but want to get a more colorful read, you want to read Pearl's The Book of Why

Intermediate: You are getting there!

  1. If you want to get started with the research, you can read Peter's Elements of Causal Inference (free), but you might find it a bit boring and too formal.
  2. If you want some great animations, I recommend Huntington-Klein's post (suggested by Jeremy Zucker)
  3. If you want a very recent book on causal inference, you might like Hernan and Robin's Causal Inference Book
  4. If you want to read a classic, I recommend CMU-based Causation, Prediction and Search.

Advanced: You made it!

  1. Pick more old and new papers from here.
  2. Send me an email if you are looking for something specific and I can help you find it!
  3. Find it on Google Scholar etc.

A map of causal inference

I created this map of causal inference to give beginner's an overview of where what kind of research is being done. Evidently, most of the research is driven by supervisor-student relationships e.g. students from MPI stay machine learning driven while students from Pearl behave similar to Pearl.