# Discovering Heterogeneous Treatment Effects in Regression Discontinuity Designs

arXiv: https://arxiv.org/abs/2106.11640 · HTML: https://mlsanity.com/paper/2106.11640/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 29 references that are in the map.
- First posted on arXiv 22 Jun 2021.

## Builds on (most cited first, 5 of 5)

- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016)
- [Recursive Partitioning for Heterogeneous Causal Effects](https://mlsanity.com/paper/1504.01132/) (2015)
- [Local Linear Forests](https://mlsanity.com/paper/1807.11408/) (2018)
- [Optimal Bandwidth Choice for Robust Bias Corrected Inference in Regression Discontinuity Designs](https://mlsanity.com/paper/1809.00236/) (2018)

## Built on by (most cited first, 1 of 1)

- [Direct Bayesian Additive Regression Trees for Conditional Average Treatment Effects in Regression Discontinuity Designs](https://mlsanity.com/paper/2603.03819/) (2026)

## Abstract (arXiv)

The paper proposes a causal supervised machine learning algorithm to uncover treatment effect heterogeneity in sharp and fuzzy regression discontinuity (RD) designs. We develop a criterion for building an honest ``regression discontinuity tree'', where each leaf contains the RD estimate of a treatment conditional on the values of some pre-treatment covariates. It is a priori unknown which covariates are relevant for capturing treatment effect heterogeneity, and it is the task of the algorithm to discover them, without invalidating inference, while employing a nonparametric estimator with expected MSE optimal bandwidth. We study the performance of the method through Monte Carlo simulations and apply it to uncover various sources of heterogeneity in the impact of attending a better secondary school in Romania.