User Guide#
The methods behind the library: scholarly references, validation against R and Python implementations, performance benchmarks, and the methodology registry documenting every estimator’s equations and edge cases.
References
Scholarly citations for every estimator and diagnostic in the library.
R Comparison
Coming from R? Side-by-side workflows and numerical validation
against did, synthdid, and fixest.
Python Comparison
How diff-diff compares to other Python causal-inference libraries.
Benchmarks
Validation results and performance benchmarks against reference implementations.
Methodology Registry
Academic foundations, equations, and documented edge cases for every estimator.
Reporting
Conventions for reporting DiD results.