# A Bayesian Additive Regression Tree Model for Learning Conditional Average Treatment Effects in Regression Discontinuity Designs

arXiv: https://arxiv.org/abs/2503.00326 · HTML: https://mlsanity.com/paper/2503.00326/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 41 references that are in the map.
- First posted on arXiv 1 Mar 2025.

## Builds on (most cited first, 13 of 13)

- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://mlsanity.com/paper/1706.03461/) (2017)
- [Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects](https://mlsanity.com/paper/1706.09523/) (2017)
- [Atlantic Causal Inference Conference (ACIC) Data Analysis Challenge 2017](https://mlsanity.com/paper/1905.09515/) (2019)
- [Stochastic tree ensembles for regularized nonlinear regression](https://mlsanity.com/paper/2002.03375/) (2020)
- [Regularization and confounding in linear regression for treatment effect estimation](https://mlsanity.com/paper/1602.02176/) (2016)
- [Regression Discontinuity Designs Using Covariates](https://mlsanity.com/paper/1809.03904/) (2018)
- [Density Regression with Bayesian Additive Regression Trees](https://mlsanity.com/paper/2112.12259/) (2021)
- [A Nonparametric Bayesian Methodology for Regression Discontinuity Designs](https://mlsanity.com/paper/1704.04858/) (2017)
- [Efficient sampling for Gaussian linear regression with arbitrary priors](https://mlsanity.com/paper/1806.05738/) (2018)
- [Optimal Bandwidth Choice for Robust Bias Corrected Inference in Regression Discontinuity Designs](https://mlsanity.com/paper/1809.00236/) (2018)
- [Hierarchical Regression Discontinuity Design: Pursuing Subgroup Treatment Effects](https://mlsanity.com/paper/2309.01404/) (2023)
- [A Bayesian Nonparametric Causal Model for Regression Discontinuity Designs](https://mlsanity.com/paper/1311.4482/) (2013)
- [Do forecasts of bankruptcy cause bankruptcy? A machine learning sensitivity analysis](https://mlsanity.com/paper/2106.04503/) (2021)

## 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)

This paper develops an effective Bayesian approach to conditional average treatment effect (CATE) estimation in regression discontinuity designs (RDD), an increasingly prevalent form of quasi-experiment that facilitates causal inference. Earlier Bayesian approaches do not easily accommodate CATE estimation while recent frequentist approaches to this problem assume a known basis expansion, a steep model specification requirement that our approach avoids. The new model is a variant of a Bayesian additive regression tree (BART) model with linear leaf-level regressions on the running variable and a treatment dummy (and their interaction). The model adaptively partitions covariate space into regions where the slope on the running variable appreciably differs, providing interpretable Bayesian inference on conditional average treatment effects near the cutoff.