Ebook: Causal Inference in Python: Applying Causal Inference in the Tech Industry (Fourth Early Release)

This book is an introduction to Causal Inference in Python, but it is not an introductory book in general. It’s introductory because I’ll focus on application, rather than rigorous proofs and theorems of causal inference; additionally, when forced to choose, I’ll opt for a simpler and intuitive explanation, rather than a complete and complex one. It is not introductory in general because I’ll assume some prior knowledge about Machine Learning (ML), statistics and programming in Python. It is not too advanced either, but I will be throwing in some terms that you should know beforehand.

How many buyers will an additional dollar of online marketing bring in? Which customers will only buy when given a discount coupon? How do you establish an optimal pricing strategy? The best way to determine how the levers at our disposal affect the business metrics we want to drive is through causal inference.

In this book, author Matheus Facure, senior data scientist at Nubank, explains the largely untapped potential of causal inference for estimating impacts and effects. Managers, data scientists, and business analysts will learn classical causal inference methods like randomized control trials (A/B tests), linear regression, propensity score, synthetic controls, and difference-in-differences. Each method is accompanied by an application in the industry to serve as a grounding example.

Here is a non exhaustive list of the things I recommend you know before reading this book:

- Basic knowledge of Python, including the most commonly used data scientists libraries: Pandas, Numpy, Matplotlib, Scikit-Learn. I come from an Economics background, so you don’t have to worry about me using very fancy code. Just make sure you know the basics pretty well.

- Knowledge of basic statistical concepts like distributions, probability, hypothesis testing, regression, noise, expected values, standard deviation, independence. I will include a statistical review in the book in case you need a refresher.

- Knowledge of basic data science concepts, like machine learning model, cross validation, overfitting and some of the most used machine learning models (gradient boosting, decision trees, linear regression, logistic regression).

With this book, you will:

Learn how to use basic concepts of causal inference
Frame a business problem as a causal inference problem
Understand how bias gets in the way of causal inference
Learn how causal effects can differ from person to person
Use repeated observations of the same customers across time to adjust for biases
Understand how causal effects differ across geographic locations
Examine noncompliance bias and effect dilution

The main audience of this book is Data Scientists who are working in the industry. If you fit this description, there is a pretty good chance that you cover the prerequisites that I’ve mentioned. Also, keep in mind that this is a broad audience, with very diverse skill sets. For this reason, I might include some note or paragraph which is meant for the most advanced reader. So don’t worry if you don’t understand every single line in this book. You’ll still be able to extract a lot from it. And maybe it will come back for a second read once you mastered some of its basics.