Causal Inference in Python (2nd Early Release)

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 detee 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.

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 to adjust for biases

Understand how causal effects differ across geographic locations

Examine noncompliance bias and effect dilution