Machine learning for the estimation of the propensity score: a simulation study

This work compares machine learning techniques (MLT) with standard logit models by analyzing the performance of the different PS estimators in matching (M) and weighting (W) via MonteCarlo simulations and assesses the efficacy of several measures of covariate balance in predicting the quality of the propensity score weighting and matching estimators.