# Assessment of Heterogeneous Treatment Effect Estimation Accuracy via Matching

arXiv: https://arxiv.org/abs/2003.03881 · HTML: https://mlsanity.com/paper/2003.03881/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2020.

## Built on by (most cited first, 4 of 4)

- [How to select predictive models for causal inference?](https://mlsanity.com/paper/2302.00370/) (2023)
- [Meta-learning for heterogeneous treatment effect estimation with closed-form solvers](https://mlsanity.com/paper/2305.11353/) (2023)
- [Differentiable Pareto-Smoothed Weighting for High-Dimensional Heterogeneous Treatment Effect Estimation](https://mlsanity.com/paper/2404.17483/) (2024)
- [Reliable Selection of Heterogeneous Treatment Effect Estimators](https://mlsanity.com/paper/2511.18464/) (2025)

## Abstract (arXiv)

We study the assessment of the accuracy of heterogeneous treatment effect (HTE) estimation, where the HTE is not directly observable so standard computation of prediction errors is not applicable. To tackle the difficulty, we propose an assessment approach by constructing pseudo-observations of the HTE based on matching. Our contributions are three-fold: first, we introduce a novel matching distance derived from proximity scores in random forests; second, we formulate the matching problem as an average minimum-cost flow problem and provide an efficient algorithm; third, we propose a match-then-split principle for the assessment with cross-validation. We demonstrate the efficacy of the assessment approach on synthetic data and data generated from a real dataset.