Applied Causal Inference Powered by ML and AI — Victor Chernozhukov, Christian Hansen, Nathan Kallus, et al.

Applied Causal Inference Powered by ML and AI is a landmark contribution to the growing intersection of machine learning and causal analysis. Written by leading academics, the book explains how data scientists and researchers can move beyond prediction to uncover cause-and-effect relationships in complex systems. It highlights the importance of understanding “why” outcomes occur, not just “what” might happen next, making it especially valuable for fields like economics, healthcare, and policy-making. The authors present cutting-edge techniques that blend AI’s predictive power with the rigor of causal inference, allowing for more reliable and actionable insights. Real-world case studies—ranging from public health interventions to financial modeling—demonstrate how these methods can be applied in practice. Unlike overly theoretical texts, this book is approachable without compromising depth, offering both conceptual clarity and technical guidance. What sets it apart is its insistence on responsibility: algorithms must not only forecast but also inform decisions that improve lives. For data scientists, statisticians, economists, and anyone working with large datasets, Applied Causal Inference Powered by ML and AI provides essential tools to transform raw data into meaningful understanding, bridging a crucial gap between artificial intelligence and human judgment.