Calibrating doubly-robust estimators with unbalanced treatment assignment

Daniele Ballinari

Economics Letters, 2024, vol. 241, issue C

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.

Keywords: Causal machine learning; Double machine learning; Average treatment effect; Unbalanced treatment assignment; Undersampling (search for similar items in EconPapers)
JEL-codes: C14 C21 C52 C55 (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:241:y:2024:i:c:s0165176524003227

DOI: 10.1016/j.econlet.2024.111838

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