rdhte: Conditional Average Treatment Effects in RD Designs

TL;DR

The paper introduces the rdhte software package for estimating and inferring heterogeneous treatment effects in sharp regression discontinuity designs, enhancing causal analysis with robust methods and bandwidth selection tools.

Contribution

It presents a new software package, rdhte, that advances the estimation and inference of heterogeneous effects in RD designs, building on recent methodological developments.

Findings

Provides robust bias-corrected inference methods.

Includes automatic bandwidth selection procedures.

Offers tools for post-estimation linear combination analysis.

Abstract

Understanding causal heterogeneous treatment effects based on pretreatment covariates is a crucial aspect of empirical work. Building on Calonico, Cattaneo, Farrell, Palomba, and Titiunik (2025), this article discusses the software package rdhte for estimation and inference of heterogeneous treatment effects in sharp regression discontinuity (RD) designs. The package includes three main commands: rdhte conducts estimation and robust bias-corrected inference for heterogeneous RD treatment effects, for a given choice of the bandwidth parameter; rdbwhte implements automatic bandwidth selection methods; and rdhte lincom computes point estimates and robust bias-corrected confidence intervals for linear combinations, a post-estimation command specifically tailored to rdhte. We also provide an overview of heterogeneous effects for sharp RD designs, give basic details on the methodology, and illustrate using an empirical application. Finally, we discuss how the package rdhte complements, and in specific cases recovers, the canonical RD package rdrobust (Calonico, Cattaneo, Farrell, and Titiunik 2017).