  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 showing that the estimator retains the DML
estimator's asymptotic properties. A simulation study illustrates the finite
sample performance of the estimator.
