Calibrating doubly-robust estimators with unbalanced treatment assignment
针对处理组样本极少导致倾向得分估计不稳的问题，提出一种对双重机器学习估计量的简单扩展：对倾向得分建模数据欠采样并校准分数，理论证明其保持渐近性质，模拟研究展示有限样本表现。
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.