Causal Inference

An expanded, fully revised edition of a leading econometrics text, offering deeper coverage, new methods, and accessible guidance on the core tools of causal inferenceCausal inference—a cornerstone of statistics and the social sciences, particularly economics—helps us determine whether one event truly causes another. The Remix expands on Scott Cunningham’s widely used Causal Inference: The Mixtape, providing a strong foundation in econometrics through accessible explanations of key design-based methods: unconfoundedness approaches, instrumental variables, regression discontinuity, and advanced panel techniques such as fixed effects, difference-in-differences, and synthetic control. This new edition adds major updates, including an extended treatment of difference-in-differences—from core concepts to advanced topics like staggered adoption and covariate adjustment—as well as fuller discussions of synthetic control, more empirical examples, and clearer pathways for readers to engage with cutting-edge methods. Written in a conversational, encouraging style, the book welcomes readers of all levels, offering breadcrumbs that connect concepts and deepen understanding. Whether you are new to causal inference or seeking to refine your tool kit, The Remix serves as both companion and manual for mastering the field’s central principles and methods.

An expanded, fully revised edition of a leading econometrics text, offering deeper coverage, new methods, and accessible guidance on the core tools of causal inferenceCausal inference—a cornerstone of statistics and the social sciences, particularly economics—helps us determine whether one event truly causes another. The Remix expands on Scott Cunningham’s widely used Causal Inference: The Mixtape, providing a strong foundation in econometrics through accessible explanations of key design-based methods: unconfoundedness approaches, instrumental variables, regression discontinuity, and advanced panel techniques such as fixed effects, difference-in-differences, and synthetic control. This new edition adds major updates, including an extended treatment of difference-in-differences—from core concepts to advanced topics like staggered adoption and covariate adjustment—as well as fuller discussions of synthetic control, more empirical examples, and clearer pathways for readers to engage with cutting-edge methods. Written in a conversational, encouraging style, the book welcomes readers of all levels, offering breadcrumbs that connect concepts and deepen understanding. Whether you are new to causal inference or seeking to refine your tool kit, The Remix serves as both companion and manual for mastering the field’s central principles and methods.