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 (AB 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.