rdlasso: Regression Discontinuity with High-Dimensional Data

Marianna Nitto and Marco Ventura

No 265, Working Papers in Public Economics from Department of Economics and Law, Sapienza University of Roma

Abstract: We present a command, rdlasso, which allows the inclusion of highdimensional covariates in Regression Discontinuity Design (RDD) settings. This command is based on the paper "Inference in Regression Discontinuity Designs with High-Dimensional Covariates" by Kreiss and Rothe (2023). The command allows for the inclusion of high-dimensional covariates in RDD for sharp and fuzzy cases, making the methodology methodology accessible to Stata users and also automating the covariate selection procedure.

Keywords: Regression Discontinuity Design; Lasso; High-dimensional data; Machine learning (search for similar items in EconPapers)
JEL-codes: C10 C20 C21 C26 (search for similar items in EconPapers)
Pages: 31
Date: 2025-10
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.dipecodir.it/wpsap/data/wp265.pdf (application/pdf)

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:sap:wpaper:wp265

Access Statistics for this paper

More papers in Working Papers in Public Economics from Department of Economics and Law, Sapienza University of Roma
Bibliographic data for series maintained by Luisa Giuriato ().