Bayesian analysis of regression discontinuity designs with heterogeneous treatment effects

Tao Kevin (), Ruppert David and Wang Y. Samuel
Additional contact information
Tao Kevin: Department of Statistics and Data Science, Cornell University, Ithaca, NY, USA
Ruppert David: Department of Statistics and Data Science, Cornell University, Ithaca, NY, USA
Wang Y. Samuel: Department of Operations Research and Information Engineering, Cornell University, Ithaca, NY, USA

Journal of Causal Inference, 2026, vol. 14, issue 1, 27

Abstract: Regression discontinuity design (RDD) is a popular framework for estimating a causal effect in settings where treatment is assigned if an observed covariate exceeds a fixed threshold. We consider estimation and inference in the common setting where the sample consists of multiple known sub-populations with potentially heterogeneous treatment effects. In the applied literature, it is common to account for heterogeneity by either fitting a parametric model or considering each sub-population separately. In contrast, we develop a Bayesian hierarchical model using Gaussian process regression which allows for non-parametric regression while borrowing information across sub-populations. We derive the posterior distribution, prove posterior consistency, and develop a Metropolis-Hastings within Gibbs sampling algorithm. In extensive simulations, we show that the proposed procedure outperforms existing methods in both estimation and inferential tasks. Finally, we apply our procedure to U.S. Senate election data and discover an incumbent party advantage which is heterogeneous over different time periods.

Keywords: Gaussian process regression; incumbency advantage; Markov chain Monte Carlo; posterior consistency; treatment heterogeneity (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/jci-2025-0024 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:causin:v:14:y:2026:i:1:p:27:n:1002

DOI: 10.1515/jci-2025-0024

Access Statistics for this article

Journal of Causal Inference is currently edited by Elias Bareinboim, Jin Tian and Iván Díaz

More articles in Journal of Causal Inference from De Gruyter
Bibliographic data for series maintained by Peter Golla ().