How many buyers will an additional dollar of online marketing attract? Which customers will only buy when given a discount coupon? How do you establish an optimal pricing strategy? Causal inference is the best way to determine how the levers at your disposal affect the business metrics you want to drive. And it only requires a few lines of Python code. In this book, author Matheus Facure 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 A/B tests, linear regression, propensity score, synthetic controls, and difference-in-differences-and modern developments such as using machine learning for heterogeneous effect estimation. Each method is illustrated by an application in the industry. This book helps you: Learn how to use basic concepts of causal inference, Frame a business problem as a causal inference problem, Understand how bias interferes with causal Inference, Learn how causal effects can differ from person to person, Use observations of the same customers across time for causal inference, Use geo and switchback experiments when randomization isn't an option, Examine noncompliance bias and effect dilution
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