Causal Inference in Python: Applying Causal Inference in the Tech Industry, 1st Edition eBook

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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. 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

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Causal Inference in Python: Applying Causal Inference in the Tech Industry 1st Edition is written by Matheus Facure and published by O’Reilly Media. The Digital and eTextbook ISBNs for Causal Inference in Python are 9781098140212, 1098140214 and the print ISBNs are 9781098140250, 1098140257. Additional ISBNs for this eTextbook include 9781098140229.