Calibrating doubly-robust estimators with unbalanced treatment assignment

• URL: http://arxiv.org/abs/2403.01585v2
• Date: Tue, 11 Jun 2024 07:11:52 GMT
• Title: Calibrating doubly-robust estimators with unbalanced treatment assignment
• Authors: Daniele Ballinari,
• Abstract summary: We propose a simple extension of the DML estimator which undersamples data for propensity score modeling. The paper provides theoretical results showing that the estimator retains the estimator's properties and calibrates scores to match the original distribution.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 showing that the estimator retains the DML estimator's asymptotic properties. A simulation study illustrates the finite sample performance of the estimator.

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