The Data Science Revolution was largely driven by the idea the we can look at the data, find patterns and leverage them to our benefit.

These ideas were fueled by the rising hopes that growing computational resources and unprecedented data availability will allow us to automate our decisions, scientific discovery and business analyses.

This turned out to work. At least to an extent.

A famous investor and mathematician, Jim Simons was able to successfully exploit predictive techniques in his investment strategies. Neural networks powered (and still do) some of the (partially) autonomous vehicles.

But there’s also a second, less visible part to this story.

Jim Simons also lost a lot of money using the predictive paradigm, and autonomous vehicles often fail when facing even slightly unusual conditions.

The three books we discuss in this blog post have one thing in common.

They all show how understanding the data generating process (rather than just looking at the patterns in the data) can help us make better decisions and enrich our understanding of the world.

Each does it differently.

1. The Book of Why: The New Science of Cause and Effect

This book, written by Judea Pearl and Dana Mackenzie is an absolute classic when it comes to causality and causal inference. It has been eye-opening to an entire generation of data scientists, researchers and practitioners.

Pearl shows the “why” behind “why” — why it is important to ask why questions and why it’s critical that we understand which methods to use in order to answer them (hint: understanding the structure of the data generating process is crucial).

Most of my podcast’s guests either started their journey into causality with this book or read it later in their career.

Highly recommended to anyone interested in improving their data skills.

🟡 “The Book of Why” by Judea Pearl & Dana Mackenzie (print, Kindle, audiobook)

2. “Antifragile: Things That Gain from Disorder”

From cherry-picking to linear models and lack of understanding of fat-tailed distributions, Nassim Taleb is a fierce critic of common practices in science and industry.

One of the theses in the book is that trying to control randomness in complex systems — although might seem beneficial in the short term — can badly backfire in the longer run.

In the book, Taleb shares his belief that talking about causes and effects might not be meaningful in case of complex non-linear and potentially cyclic systems.

I am not convinced by his pessimistic position here. We know that meaningful interventions in dynamical systems are possible, but we need to know what we’re doing (Naftali Weinberger explains it here).

What Taleb calls “naive interventions” can lead to dramatic, unwanted consequences.

Another important concept in the book are fat and long-tailed distributions that can easily derail any traditional learning algorithm trained on finite-sized samples.

Fat and long tails are critically important in most complex areas from finance to autonomous driving.

Great and mind-expanding read.

🟡 “Antifragile” by Nassim Nicholas Taleb (print, Kindle, audiobook)

3. “Chaos: Making a New Science”

In the age of machine learning, we got used to the thought that if something is unpredictable, it clearly lacks structure — and maybe collecting measurements of more variables could make it more predictable.

The truth is that chaotic systems can produce chaotic behavior very systematically, based on a set of (possibly) very simple rules.

James Gleick’s book is an excellent introduction to chaos theory, dynamical systems and complexity. His engaging style and love for a good story (from Oppenheimer to Lorenz and much more) make it a great read!

If you were ever interested in fractals, emergence, complexity or chaos — this is a must-read.

🟡 “Chaos” by James Gleick (print, Kindle, audiobook)

Each of these books brings a unique perspective to the table, and each presents an idea that questions a broadly accepted status quo.

Taken together, these books are a great inspiration to question the assumptions that our contemporary data culture takes for granted.

Sometimes just fitting models to data is not enough to answer the questions that are the most important to us.

Understanding this is power.

I hope these mind-expanding books will inspire you as much as they inspired me (to say the least, I wouldn’t have written my book if I hadn’t read “The Book of Why”)

Finally, I‘d love to learn from you — what are your favorite mind-expanding books?

PS: The best way to let me know if you liked this story is by clapping. You can clap more than once 👏🏼👏🏼👏🏼

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This article contains affiliate links — if you decide to purchase a book using one of them a small part of the revenue will help me create more free content for you.

Update 2025: You can now check my free course on causality in the context of experimentation and modern machine learning here: https://causalsecrets.com/

Introduction

Recent years brought a sharp increase in interest in causal methods in the research community and in the industry. One of the challenges that people entering the field face is a lack of standardized resources and terminology. Causality research has been scattered and divided into sub-fields for decades. One of the consequences of this fact is that many newcomers feel overwhelmed and confused when they enter the field.

I was in the same spot when I started.

Today, I have my own book on causality in Python, where I summarized my journey and translated the most important causal concepts into Python code, but let’s not get ahead of ourselves.

In this post I want to share with you six causal books that allowed me to structure and speed-up my causal journey. I hope they will help you achieve the same!

And yes, you got it right, you can get 3 of these books for free, 100% legally if you choose so! 😯

For every book I’ll provide you with 5 bullet points highlighting the most important topics covered in the book. I’ll also provide you with links to get a copy and/or a free copy of a book if it’s available.

Let’s start!

1. Starting Strong: “The Book of Why”

“The Book of Why” by the godfather of modern causality Judea Pearl and his co-author, former mathematician Dana Mackenzie is a starting point for many and not by accident.

You can think of it as a comprehensive introduction to the field. It’s a mixture of theory, history, storytelling, math and practical exercises. If that sounds like a lot, don’t worry, it’s really well-structured and fun to read! The authors cover the history of causality, the basic theory behind Pearl’s do-calculus and share inspiring examples of the applications of causal inference in the real life and science. We also get many useful comparisons between do-calculus and potential outcomes frameworks. They will not only let you learn the basics of the latter, but also to grasp the elementary vocabulary that will help you orientate yourself in the broader causal world. Last, but not least the narrative is build around the concept of The Ladder of Causation — a powerful framework that helps the reader to clearly distinguish between associative, interventional and counterfactual modes of analysis.

Another great thing about this book is that it’s available as an audiobook!

What you’ll learn:

• History of causality
• The Ladder of Causation
• Basics of do-calculus
• Selected concepts of the potential outcomes framework
• Useful applications of causal inference

Get a copy:

• Paper (Amazon)
• Kindle (Amazon)
• Audiobook (Audible)

Amazon links in this article are affiliate links. For every purchase made using these links I’ll receive a small amount of the transaction fee that will support my writing. At the same time it does not change the price for you. Thank you!

2. Your Turn: “Causal Inference in Statistics — A Primer”

When I finished reading “The Book of Why” I wanted more! At the same time, I was not sure which direction to take. I asked my network on Twitter for their recommendations. Interestingly, the first reply I received was from Judea Pearl who recommended “Causal Inference in Statistics: A Primer” to me. Whose recommendation could be better?

It’s a great book with an amazing approach to teaching. It’s relatively short — just a little over 120 pages, yet very content-rich, including exercises.

The book is divided into 4 parts:

• A review of basic statistics and probability,
• Introduction to graphical models,
• A discussion on interventions,
• A discussion on couterfactuals.

The book will give you really solid foundations, especially if you follow with the exercises! I want to add that the theory and practice are mostly limited to discrete and linear cases, yet I see this as an advantage. It allows you to focus on what’s important from the causal point of view rather than being distracted by complex math or fancy estimation methods.

The book also covers more advanced topics like mediation, direct and indirect effects, probability of sufficiency and necessity and teaches you how to compute counterfactuals by hand (how cool is that?).

I read it on my Kindle but a paper version is also available.

What you’ll learn:

• Graphical models
• Interventions as graph surgery
• Back-door and front-door criteria and inverse probability weighting
• Counterfactuals
• Mediation, probability of necessity and probability of sufficiency

Get a copy:

• Paper (Amazon)
• Kindle (Amazon)

3. Get more perspective: “Elements of Causal Inference”

After finishing “Causal Inference in Statistics: A Primer”, I was hungry for more! In particular, I wanted to learn more about causal discovery.

“Elements of Causal Inference” by Peters and colleagues is the first book on our list that goes beyond traditional causal inference and extends to causal structure learning (aka causal discovery). This might sound strange, because the term causal inference is written is glaring large yellow letters on the cover. The reason for this is that the authors use the term in a broader meaning that also includes causal discovery (do you remember what did we say about standardized terminology in the intro? — that’s just a tip of the iceberg!)

The book covers differences between purely statistical and causal models, assumptions for causal inference, bi-variate and multivariate models, semi-supervised learning, reinforcement learning, domain adaptation and time series models, all seen through causal lens.

Math goes beyond discrete and linear cases and you can meet integrals and derivatives here and there. The authors share some examples and insights from the world of physics — a nice addition to popular examples from the fields of social sciences and epidemiology.

Is the book a complete handbook for causal inference and discovery? As the authors state in the introduction — no, rather their “personal taste influenced the choice of topics” (Peters et al., 2017, p. xii) and in my opinion it makes this book really unique!

Before we conclude, let me share two more thoughts with you. If you look for a book that is full of real-world use cases and solutions to practical problems —you won’t find it here. If on the other hand you aim at deepening your understanding of the mechanics of causal inference and discovery, in particular in relation to machine learning, this might be a really good shot! 🤘🏼

If you’re not sure, don’t worry! You can get this book for free 😯 in a PDF format and check if it’s a good fit for you (link below).

What you’ll learn:

• Theory behind (some of the) causal discovery methods
• Causal inference & discovery for bi-variate models
• Causal inference & discovery for multivariate models
• Causality vs episodic reinforcement learning
• Causality and time series

Get a copy:

• Paper (Amazon)
• Kindle (Amazon)
• Free PDF (OAPEN)

Causal Python: 3 Simple Techniques to Jump-Start Your Causal Inference Journey Today

4. Deep Dive: “Causality — Models, Reasoning and Inference”

Pearl’s “Causality: Models, Reasoning and Inference” brings over 400 pages of causal deep dive. The book covers graphical models, d-separation, Bayesian causal models, structural causal models, structural equation models (SEM), model identification, assumptions behind causal inference, complete rules of do-calculus, in-depth discussions on interventions and counterfactuals, probability of causation and more.

In addition, the book discusses a bit of causal discovery. Chapter 2 covers two structure learning algorithms proposed by Pearl and Verma: IC and IC*. In many places in the book, you can find comparisons between graph-based approach to causality and Rubin’s potential outcomes framework, which allows you to deepen your understanding of (inter)relations between the two.

The last part contains over 60 pages of reflections and discussions with readers.

All of this adds up to a very comprehensive resource on causality that you can use as your go-to reference on the topic ⚡⚡⚡

What you’ll learn:

• Assumptions behind causal inference
• Do-calculus (in-depth)
• Casual discovery (limited scope)
• Probability of causation
• Interventions and couterfactuals (in-depth)

Get a copy:

• Paper (Amazon)
• Kindle (Amazon)

5. The World of Econometrics: “Causal Inference — The Mixtape”

Do you feel like something more practical?

Scott Cunningham’s “Causal Inference — The Mixtape” is the first book on the list with the main focus on real-world applications of causal inference methods. The book provides us with a ton of great practical examples of causal inference applications from the fields of economics, social policy, epidemiology and more.

The narrative is enriched with frequent references to hip-hop culture and quotes from hip-hop artists (my favorite example is a quote from Chance the Rapper used to explain how to find good instruments when using Instrumental Variables technique ♥️). Each section of the book is accompanied by Stata and R code snippets. Python code is available in the book’s repository and in the online version of the book (link below).

The main focus of the book is on the methods popular in contemporary econometrics: regression discontinuity, instrumental variables, difference-in-differences and synthetic control estimator. The book contains just enough math to give you a solid understanding of the discussed methods. Not too much, not too little.

The book covers both — randomized and natural — experiments and provides us with a comprehensive overview of potential outcomes framework.

🤫Psst! Scott Cunningham is also an author of a popular podcast on causality. You can find it here.

What you’ll learn:

• Natural experiments
• Potential outcomes
• Regression discontinuity
• Instrumental variables
• Difference-in-differences & synthetic control estimator

Get a copy:

• Paper (Amazon)
• Kindle (Amazon)
• Free Online Book

6. A Unifying Framework? “What If?”

Last, but not least, the sixth book I want to recommend to you comes from Harvard’s Miguel Hernán and James Robins. Both authors are seasoned researchers and well-known figures in the world of epidemiology.

The book is divided in three parts:

• Causal inference without models
• Causal inference with models
• Causal inference from complex longitudinal data

Out of the six, this is probably the most balanced book in terms of how much space is dedicated to graph-based vs potential outcomes frameworks.

The authors provide us with great insights on interactions in the context of interventions, selection bias and more.

A part of the uniqueness of the book lies in the discussions of structural nested models, causal survival analysis and causal effects of time-varying treatments. It’s a great read if you want to broaden your horizons!

The book comes with code in SAS, Stata, R, Python ♥️ and Julia. Links to all repositories are available on book’s website.

Currently the print version is not available.

What you’ll learn:

• Interactions
• Selection bias
• Structural nested models
• Causal survival analysis
• Causal effects of time-varying treatments

Get a copy:

• Free PDF

Wrapping It Up!

In this post we discussed six causal books that will get you from beginner to advanced in causality. Each book offers something unique that you cannot find in others. Three of the books mentioned in this article get be read for free — either online or as a PDF.

If you want to jump-start your causal journey in Python today, check this post.

Good luck with your causal journey 💪 and let me know your thoughts in the comments and/or reach out on LinkedIn!

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