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Testing the Accuracy of Regression Discontinuity Analysis Using Experimental Benchmarks

  • Donald P. Green (a1), Terence Y. Leong (a2), Holger L. Kern (a3), Alan S. Gerber (a1) and Christopher W. Larimer (a4)...

Regression discontinuity (RD) designs enable researchers to estimate causal effects using observational data. These causal effects are identified at the point of discontinuity that distinguishes those observations that do or do not receive the treatment. One challenge in applying RD in practice is that data may be sparse in the immediate vicinity of the discontinuity. Expanding the analysis to observations outside this immediate vicinity may improve the statistical precision with which treatment effects are estimated, but including more distant observations also increases the risk of bias. Model specification is another source of uncertainty; as the bandwidth around the cutoff point expands, linear approximations may break down, requiring more flexible functional forms. Using data from a large randomized experiment conducted by Gerber, Green, and Larimer (2008), this study attempts to recover an experimental benchmark using RD and assesses the uncertainty introduced by various aspects of model and bandwidth selection. More generally, we demonstrate how experimental benchmarks can be used to gauge and improve the reliability of RD analyses.

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Authors' note: The authors are grateful to Mark Grebner, who designed and implemented the mailing campaign analyzed here, and Joshua Haselkorn, Jonnah Hollander, and Celia Paris, who provided research assistance.

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This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

Jinyong Hahn , Petra Todd , and Wilbert Van der Klaauw . 2001. Identification and estimation of treatment effects with a regression discontinuity design. Econometrica 69: 201–9.

Jinyong Hahn , Petra Todd , and Wilbert Van der Klaauw . 2001. Identification and estimation of treatment effects with a regression discontinuity design. Econometrica 69: 201–9.

Guido Imbens , and Karthik Kalyanaraman . 2009. Optimal bandwidth choice for the regression discontinuity estimator. Unpublished manuscript, Department of Economics, Harvard University.

Guido W. Imbens , and Thomas Lemieux . 2008. Regression discontinuity designs: A guide to practice. Journal of Econometrics 142: 615–35.

David S. Lee 2008. Randomized experiments from non-random selection in U.S. house elections. Journal of Econometrics 142: 675–97.

David S. Lee , and Thomas Lemieux . 2009. Regression discontinuity designs in economics. National Bureau of Economic Research Working Paper 14723, Cambridge, MA.

J. Ludwig , and D. L. Miller 2007. Does head start improve children's life chances? Evidence from a regression discontinuity design. Quarterly Journal of Economics 122: 159208.

Justin McCrary . 2008. Manipulation of the running variable in the regression discontinuity design: A density test. Journal of Econometrics 142: 698714.

Donald L. Thistlethwaite , and Donald T. Campbell 1960. Regression-discontinuity analysis: an alternative to the ex-post facto experiment. Journal of Educational Psychology 51: 309–17.

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Political Analysis
  • ISSN: 1047-1987
  • EISSN: 1476-4989
  • URL: /core/journals/political-analysis
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