D

double-machine-learning

Python research code for sensitivity analysis in double machine learning. A twin design, in which the hidden confounder copies an observed benchmark covariate, tests a benchmark-calibrated omitted-variable-bias bound when the benchmark is itself estimated, with an application to 401(k) eligibility and a mathematical supplement.