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“This book will be a canonical reference for differences-in-differences-style designs (and beyond). It offers a unique perspective not currently covered by any book at a graduate level, but which is nevertheless a central part of the research landscape in applied microeconomics and econometrics.”—Damian Clarke, University of Exeter and University of Chile

“A highly ambitious, impressively comprehensive, and methodologically sophisticated book that fills a real gap in the DiD literature. The authors display a superior grasp of the literature and pair that with strong empirical illustrations of key points, encouraging readers to actively work through empirical examples while simultaneously checking their conceptual understanding.”—L. Jason Anastasopoulos, University of Georgia

“The main strength of this excellent book lies in the comprehensive and coherent coverage of a wide range of DID designs, all presented in a unified notation. Another major advantage is the set of ready-to-use empirical examples accompanied by software code, which makes the material very accessible for applied researchers.”—Martin Huber, University of Fribourg

“Two of the masters of modern differences-in-differences provide both a theoretical and practical guide to dealing with all the messy real-world issues that come up in many applications of this extremely popular method. Continuous or multivalued treatments, a small number of treated units, when to include control variables, treatments that switch on- and then off-again—this fantastic book provides theory, practical examples, and code to help you handle it all.”—David McKenzie, Development Research Group, World Bank

“Differences-in-differences is now an indispensable framework for quantitative social science research. Two of the field’s foremost experts, Cl茅ment de Chaisemartin and Xavier D’Haultfoeuille, take readers from the practical essentials all the way to the research frontier, making this book an invaluable resource for both newcomers and experienced researchers.”—Kosuke Imai, Harvard University

“A brilliant and insightful deep dive into causal inference with differences-in-differences written by two of the world’s leading researchers in this rapidly expanding field. This book provides a comprehensive exposition from the classical DID design through to the wealth of recent designs including those that address staggered and heterogeneous adoption. Assumptions are clearly explained, compelling examples are presented throughout, and extremely helpful code is provided. A must read for all those interested in causal inference.”—Richard Blundell, University College London