Calibrating doubly-robust estimators with unbalanced treatment assignment

arXiv:2403.01585v1 Announce Type: cross
Abstract: Machine learning methods, particularly the double machine learning (DML) estimator (Chernozhukov et al., 2018), are increasingly popular for the estimation of the average treatment effect (ATE). However, datasets often exhibit unbalanced treatment assignments where only a few observations are treated, leading to unstable propensity score estimations. We propose a simple extension of the DML estimator which undersamples data for propensity score modeling and calibrates scores to match the original distribution. The paper provides theoretical results …

abstract arxiv datasets econ.em estimations machine machine learning popular robust stat.ml treatment type