# Yes! Six Causality Books That Will Get You From Zero to Advanced (2024)

## …and you can get 3 of them completely for **free** if you want! 🤗

## 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

**and**

*do*-calculus**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

**— a powerful framework that helps the reader to clearly distinguish between associative, interventional and counterfactual modes of analysis.**

*The Ladder of Causation*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:**

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. The first reply I got was from Judea Pearl (*sic!*) 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:**

## 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:**

## 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

**, in-depth discussions on**

*do*-calculus**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**(in-depth)*Do*-calculus**Casual discovery**(limited scope)**Probability of causation****Interventions and couterfactuals**(in-depth)

**Get a copy:**

## 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 s**ynthetic 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:**

## 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. According to the information on Amazon Canada it will be **available** in April 2024.

**What you’ll learn:**

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

**Get a copy:**

# 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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