# Causality resources

** Published:**

Good resources matter, a *lot*.

# The need for ~~speed~~ good resources

One of the most important steps

beforestarting to study a topic is to collect some good resources, Scott H. Young argues in his 2019 book Ultralearning.

As a side note, I might—I mean, I will do this for sure—litter my posts with book references (actually, I am even planning to share the books I am reading with you in the near future).

But let’s not permit ourselves to be distracted, let’s get to the business!

As I agree with Scott, I invested some time to compile a list of materials, including lectures, books, MOOCs (Massive Open Online Courses). I have already used some of these, but at least I familiarized myself with it enough to deem each and every item valuable. Let’s get to the details, shall we?

# Causality resources

## Books

- Judea Pearl and Dana Mackenzie,
*The Book of Why*: a gentle non-technical introduction into the topic with a lot of examples; definitely worth reading! - Judea Pearl,
*Causality*(2009) : this is THE classic textbook on the topic. It is not a very easy read, but it contains a vast amount of theoretical background. Nonetheless, a lot of times it refers to other papers for proofs, etc. - I found this reasonable but inconvenient.**Spoiler:**one of the goals of this blog is to get through this book and explain the concepts to develop a better understanding. - Bernhard Schölkopf, Dominik Janzing, and Jonas Peters, Elements of Causal Inference: a recent
**open-access**book that is keen on the theory of causal inference. - Brady Neal,
*Introduction to Causal Inference from a Machine Learning Perspective*: this is the lecture notes for the online course*Introduction to Causal Inference*(listed in the MOOCs and online lectures section). Due to being lecture notes, I find this book to be more clearly cut, more structured, but it covers less material. Unfortunately, as of today, the book is still in progress-the most interesting (and advanced) chapters are still on their way. - Miguel A. Hernán, James M. Robins, Causal Inference: What if: this
**open-access**book is mainly aimed at social scientists and medical researchers (in the sense that the authors mainly discuss causality with concepts widely-used in those domains). Nevertheless, graphs (the choice of weapon for computer scientists) are also discussed in this very detailed book.

## MOOCs and online lectures

- Probabilistic Graphical Models Specialization: this three-course feast presented by the famouse Daphne Koller provides you with the basics of traditional methods in graphical models. Although not causality-specific in its entirety, it lays the foundations, so definitely worth attending-it even provides programming assigments so you can get familiar with the methods. Aaaand it’s totally
**free**(if you are satisfied without a Coursera certificate). - Introduction to Causal Inference: a causal inference course covering the fundamental topics. As it has the lecture notes released (see Books), you should be well-equipped to take the challenge to become a causality expert.
- Bayesian Statistics: From Concept to Data Analysis: an introductory course into Bayesian statistics. Besides covering the base (such as Bayes’ theorem, Bayesian inference), it also discusses more advances topics as uninformative and Jeffreys priors. As a bonus, we get some insight how to apply the concepts in R.
- Jonas Peters’ Lectures on Causality Part 1, Part 2, Part 3, Part 4 from 2017, held at MIT: these are superb introductory lectures on the topics with a sense of humor and a lot of examples.
- Online Causal Inference Seminar: the name tells everything. This interdisciplinary seminar will provide you the viewpoints of different scientific fields on causality. Let’s exploit this unique opportunity to avoid falling into the bias of a single field’s approach. The recordings are oalso available so you cannot make any excuse!

## Flashcards

- Probabilistic Graphical Models 1
- Probabilistic Graphical Models 2
- Probabilistic Graphical Models 3
- Bayesian Statistics: From Concept to Data Analysis
- Bayesian Statistics: Techniques and Models

*P.S.: if you find any error in the decks, please contact me*