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When you know the cause of an event, you can affect its outcome. This accessible introduction to causal inference shows you how to determine causality and estimate effects using statistics and machine learning.
Causal Inference for Data Science introduces data-centric techniques and methodologies you can use to estimate causal effects. The book dives into the relationship between causal inference and machine learning and the limitations of both.
The practical techniques presented in this unique book are accessible to anyone with intermediate data science skills and require no advanced statistics! The numerous insightful examples show you how to put causal inference into practice in the real world. You’ll assess the performance of advertising platforms, choose the health treatments with the most positive impact, and learn how to approach the delicate art of product pricing from a causal inference perspective.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
Causal Inference for Data Science
by Aleix Ruiz de Villa RobertFormat: Nonspecific Binding
Pub. Date: 2025-02-18
Publisher(s): Simon & Schuster
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Summary
Author Biography
Aleix Ruiz de Villa is a freelance data science consultant with a PhD in mathematical analysis from the Universitat Autonoma de Barcelona. Aleix has worked in the journalism, retail, transportation and software development industries. He is the founder of the Barcelona Data Science & Machine Learning Meetup.
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