# Calibrating doubly-robust estimators with unbalanced treatment assignment

arXiv: https://arxiv.org/abs/2403.01585 · HTML: https://mlsanity.com/paper/2403.01585/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 32 references that are in the map.
- First posted on arXiv 3 Mar 2024.

## Builds on (most cited first, 7 of 7)

- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://mlsanity.com/paper/1706.03461/) (2017)
- [Quasi-Oracle Estimation of Heterogeneous Treatment Effects](https://mlsanity.com/paper/1712.04912/) (2017)
- [A Survey on Causal Inference](https://mlsanity.com/paper/2002.02770/) (2020)
- [Machine Learning Methods Economists Should Know About](https://mlsanity.com/paper/1903.10075/) (2019)
- [Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations](https://mlsanity.com/paper/1309.4686/) (2013)
- [Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study](https://mlsanity.com/paper/2402.04674/) (2024)
- [High-Dimensional L2Boosting: Rate of Convergence](https://mlsanity.com/paper/1602.08927/) (2016)

## Built on by (most cited first, 2 of 2)

- [Improving the Finite Sample Estimation of Average Treatment Effects using Double/Debiased Machine Learning with Propensity Score Calibration](https://mlsanity.com/paper/2409.04874/) (2024)
- [Synthetic Counterfactual Labels for Efficient Conformal Counterfactual Inference](https://mlsanity.com/paper/2509.04112/) (2025)

## Abstract (arXiv)

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.