Causal Inference for Data Science (MEAP V04)

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.
 
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