Applied Causal Inference Powered by ML and AI
Overview
Paper Summary
This book introduces the application of machine learning methods for causal inference, specifically focusing on how predictive tools like Lasso, random forests, and deep neural networks can be used for causal analysis. The authors explain key concepts in both predictive and causal inference and provide real-data examples with accompanying code notebooks. The book assumes some background in econometrics and focuses primarily on econometric applications.
Explain Like I'm Five
This book teaches how to use machine learning to understand cause and effect, like how changing a product's price affects sales.
Possible Conflicts of Interest
None identified
Identified Limitations
Rating Explanation
This book provides a valuable introduction to the intersection of causal inference and machine learning. It covers both theoretical foundations and practical applications with code examples. While lengthy and somewhat specific to econometrics, its strengths outweigh its weaknesses.
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