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.

In Causal Inference for Data Science you will learn how to:

• Model reality using causal graphs
• Estimate causal effects using statistical and machine learning techniques
• Determine when to use A/B tests, causal inference, and machine learning
• Explain and assess objectives, assumptions, risks, and limitations
• Determine if you have enough variables for your analysis

It's possible to predict events without knowing what causes them. Understanding causality allows you both to make data-driven predictions and also intervene to affect the outcomes. Causal Inference for Data Science shows you how to build data science tools that can identify the root cause of trends and events. You'll learn how to interpret historical data, understand customer behaviors, and empower management to apply optimal decisions.

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 and Machine Learning Meetup.

Book Information
ISBN 9781633439658
Author David Sweet
Format Hardback
Page Count 381
Imprint Manning Publications
Publisher Manning Publications