Causal Inference in Python

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.

Matheus Facure is an economist and senior data scientist at Nubank, the biggest FinTech company outside Asia. He has successfully applied causal inference in a wide range of business scenarios, from automated and real-time interest and credit decision making, to cross-sell emails and optimizing marketing budgets. He is also the author of Causal Inference for the Brave and True,...

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