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Calibrating doubly-robust estimators with unbalanced treatment assignment

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  • Daniele Ballinari
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.

Suggested Citation

  • Daniele Ballinari, 2024. "Calibrating doubly-robust estimators with unbalanced treatment assignment," Papers 2403.01585, arXiv.org, revised Jun 2024.
    • Handle: RePEc:arx:papers:2403.01585
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    References listed on IDEAS

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    Cited by:

    1. Daniele Ballinari & Nora Bearth, 2024. "Improving the Finite Sample Performance of Double/Debiased Machine Learning with Propensity Score Calibration," Papers 2409.04874, arXiv.org.

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    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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