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about the technology

A/B tests or controlled trials are expensive and often unfeasible in a business environment. Causal inference is a powerful methodology that allows a data scientist to identify causes from data, even when no experiment or test has been performed. Using causal methods increases the level of confidence in business decision making by clearly connecting causes and effects.

about the book

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.

about the reader

For data scientists, machine learning engineers, statisticians and economists who want to learn a machine learning approach to causal inference.

about the author

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.