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Treatment Effect Heterogeneity in Regression Discontinuity Designs

Sebastian Calonico, Matias D. Cattaneo, Max H. Farrell, Filippo Palomba, Rocio Titiunik

arXiv 17 Mar 2025 · Econometrics

arXiv:2503.13696 · PDF · DOI · OpenAlex · Extracted main text

Abstract

Empirical studies using Regression Discontinuity (RD) designs often explore heterogeneous treatment effects based on pretreatment covariates, even though no formal statistical methods exist for such analyses. This has led to the widespread use of ad hoc approaches in applications. Motivated by common empirical practice, we develop a unified, theoretically grounded framework for RD heterogeneity analysis. We show that a fully interacted local linear (in functional parameters) model effectively captures heterogeneity while still being tractable and interpretable in applications. The model structure holds without loss of generality for discrete covariates. Although our proposed model is potentially restrictive for continuous covariates, it naturally aligns with standard empirical practice and offers a causal interpretation for RD applications. We establish principled bandwidth selection and robust bias-corrected inference methods to analyze heterogeneous treatment effects and test group differences. We provide companion software to facilitate implementation of our results. An empirical application illustrates the practical relevance of our methods.

Citation extraction

18
references
18
in-text mentions
18
distinct cited
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17,790
main-text words

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Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Adams, A., Kluender, R., Mahoney, N., Wang, J., Wong, F., and Yin, W (2022) The Impact of Financial Assistance Programs on Health Care Utilization: Evidence from Kaiser Permanente0.40511100%
2Akhtari, M., Moreira, D., and Trucco, L (2022) Political Turnover, Bureaucratic Turnover, and the Quality of Public Services0.40511100%
3Asher, S., and Novosad, P (2020) Rural Roads and Local Economic Development0.40511100%
4Brollo, F., Nannicini, T., Perotti, R., and Tabellini, G (2013) The Political Resource Curse0.40511100%
5Cameron, A. C., and Miller, D. L (2015) A practitioner’s guide to cluster-robust inference0.40511100%
6Dell, M (2015) Trafficking Networks and the Mexican Drug War0.40511100%
7Han, H.-W., Lien, H.-M., and Yang, T.-T (2020) Patient Cost-Sharing and Healthcare Utilization in Early Childhood: Evidence from a Regression Discontinuity Design0.40511100%
8Huh, J., and Reif, J (2021) Teenage Driving, Mortality, and Risky Behaviors0.40511100%
9Jones, M., Kondylis, F., Loeser, J., and Magruder, J (2022) Factor Market Failures and the Adoption of Irrigation in Rwanda0.40511100%
10Lindo, J. M., Sanders, N. J., and Oreopoulos, P (2010) Ability, Gender, and Performance Standards: Evidence from Academic Probation0.40511100%

Showing the top 10 of 18 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1rdhte: Conditional Average Treatment Effects in RD Designs1.000134
2Leveraging Covariates in Regression Discontinuity Designs0.51121
3Boundary Discontinuity Designs: Theory and Practice0.40511