Causal Book

Causal Book: Design Patterns in Causal Inference This project is not meant to replace the many great books on causal inference methods. Instead, it strives to accomplish two goals in an interactive format that presents all of the book's content in a network of ideas, concepts, and applications: 1. To present solutions of the same conceptual model for problems requiring causal inference (on the same data, sometimes using the same estimators) using different approaches: Statistics (in a very narrow sense), Machine Learning, and Bayesian. The applications include complete R and Python code, and in some cases, there are comparative evaluations of the two. 2. To discuss the intricacies of modeling data for causal inference and lesser known and sometimes puzzling situations along the way, such as larger coefficients in IV models, negative R-squared values, or conflicting corrections for standard errors. These are some intriguing cases with surprising (or seemingly surprising) results encountered when applying the patterns.