Causal Inference in Python Applying Causal Inference in the Tech Industry

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 time to adjust for biases
* Understand how causal effects differ across geographic locations
* Examine noncompliance bias and effect dilution