oreilly Causal Inference in Python Paperback
In this book, author Matheus , senior data scientist at , 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. While we work to ensure that product information is correct, on occasion manufacturers may alter their ingredient lists. Actual product packaging and materials may contain more and/or different information than that shown on our web site. We recommend that you do not solely rely on the information presented and that you alw.
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