Sebastian Calonico, Matias D. Cattaneo, Max H. Farrell, Filippo Palomba, Rocio Titiunik
arXiv 1 Jul 2025 · Econometrics
arXiv:2507.01128 · PDF · DOI · OpenAlex · Extracted main text
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).
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Calonico, S., M. D. Cattaneo, M. H. Farrell, F. Palomba, and R. Titi… (2025) Treatment Effect Heterogeneity in Regression Discontinuity Designs self | 1.000 | 13 | 4 | 100% |
| 2 | Calonico, S., M. D. Cattaneo, M. H. Farrell, and R. Titiunik (2017) rdrobust: Software for Regression Discontinuity Designs self | 0.928 | 4 | 4 | 100% |
| 3 | width30.25006ptheight2.62222ptdepth-2.25222pt (2020) Optimal Bandwidth Choice for Robust Bias Corrected Inference in Regression Discontinuity Designs | 0.928 | 4 | 3 | 100% |
| 4 | width30.25006ptheight2.62222ptdepth-2.25222pt (2022) Coverage Error Optimal Confidence Intervals for Local Polynomial Regression | 0.843 | 3 | 3 | 100% |
| 5 | width30.25006ptheight2.62222ptdepth-2.25222pt (2019) Regression Discontinuity Designs using Covariates | 0.737 | 3 | 2 | 100% |
| 6 | Calonico, S., M. D. Cattaneo, and R. Titiunik (2014) Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs self | 0.737 | 3 | 2 | 100% |
| 7 | Calonico, S., M. D. Cattaneo, and M. H. Farrell (2018) On the Effect of Bias Estimation on Coverage Accuracy in Nonparametric Inference self | 0.644 | 2 | 2 | 100% |
| 8 | Granzier, R., V. Pons, and C. Tricaud (2023) Coordination and Bandwagon Effects: How Past Rankings Shape the Behavior of Voters and Candidates | 0.644 | 2 | 2 | 100% |
| 9 | Arai, Y., and H. Ichimura (2018) Simultaneous Selection of Optimal Bandwidths for the Sharp Regression Discontinuity Estimator | 0.405 | 1 | 1 | 100% |
| 10 | Cattaneo, M. D., R. K. Crump, M. H. Farrell, and Y. Feng (2024) On Binscatter self | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 23 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Leveraging Covariates in Regression Discontinuity Designs | 0.405 | 1 | 1 |