User Guide#
The methods behind the library: scholarly references, validation against R and Python implementations, performance benchmarks, the migration guide for the upcoming 4.0 release, and the methodology registry documenting every estimator’s equations and edge cases.
Scholarly citations for every estimator and diagnostic in the library.
Coming from R? Side-by-side workflows and numerical validation
against did, synthdid, and fixest.
How diff-diff compares to other Python causal-inference libraries.
Every breaking change in the upcoming 4.0 release, with the one-line fix for each and a codemod table for the mechanical renames.
Validation results and performance benchmarks against reference implementations.
Academic foundations, equations, and documented edge cases for every estimator.
Conventions for reporting DiD results.