Getting away from the cutoff in regression discontinuity designs

Regression discontinuity (RD) designs are highly popular in economic research because of their strong internal validity and straightforward intuition. While RD estimates are local in nature, several recent articles propose methods that generalize RD estimates to units outside a small neighborhood of the cutoff. In this article, I introduce the getaway package, which implements the method proposed by Angrist and Rokkanen (2015, Journal of the American Statistical Association 110: 1331-1344) to extrapolate treatment-effect estimates “away from the cutoff”, relying on a classical unconfoundedness condition. Additionally, the package features a data-driven algorithm designed to identify a set of covariates that fulfills the unconfoundedness assumption. It also incorporates a toolkit intended for testing and visualization purposes.

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Publication Details

Journal
The Stata Journal Promoting communications on statistics and Stata
Published
2024-09-01
DOI
https://doi.org/10.1177/1536867x241276108
Citations
5
Primary Topic
Statistical Methods and Inference
Type
article
Field-Weighted Citation Impact
1.42
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article

Getting away from the cutoff in regression discontinuity designs

Filippo Palomba
5 citations
The Stata Journal Promoting communications on statistics and Stata
Statistical Methods and Inference
1.42
article

Getting away from the cutoff in regression discontinuity designs

Filippo Palomba
article en
5 citations

Abstract

Regression discontinuity (RD) designs are highly popular in economic research because of their strong internal validity and straightforward intuition. While RD estimates are local in nature, several recent articles propose methods that generalize RD estimates to units outside a small neighborhood of the cutoff. In this article, I introduce the getaway package, which implements the method proposed by Angrist and Rokkanen (2015, Journal of the American Statistical Association 110: 1331-1344) to extrapolate treatment-effect estimates “away from the cutoff”, relying on a classical unconfoundedness condition. Additionally, the package features a data-driven algorithm designed to identify a set of covariates that fulfills the unconfoundedness assumption. It also incorporates a toolkit intended for testing and visualization purposes.

The Stata Journal Promoting communications on statistics and StataVol. 24(3)
Princeton University (US)
Openalex Percentile: Top 17%
Statistical Methods and Inference
1.42
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