Improving the Finite Sample Estimation of Average Treatment Effects Using Double/Debiased Machine Learning With Propensity Score Calibration

Double/debiased machine learning (DML) uses for estimating an average treatment effect (ATE) a double‐robust score function that relies on the prediction of nuisance functions, such as the propensity score, which is the probability of treatment assignment given covariates. Estimators relying on double‐robust score functions are highly sensitive to errors in propensity score predictions. Machine learning algorithms have been found to produce models that often overestimate or underestimate these probabilities. Several calibration approaches have been proposed to improve probabilistic forecasts of machine learners. This paper explores their integration into the DML framework, showing via simulations that using calibrated propensity scores significantly reduces the root mean squared error of ATE estimates in finite samples while preserving DML's asymptotic properties.