Regression Discontinuity Design with Covariates

Markus Frölich ()

No 3024, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: In this paper, the regression discontinuity design (RDD) is generalized to account for differences in observed covariates X in a fully nonparametric way. It is shown that the treatment effect can be estimated at the rate for one-dimensional nonparametric regression irrespective of the dimension of X. It thus extends the analysis of Hahn, Todd, and van der Klaauw (2001) and Porter (2003), who examined identification and estimation without covariates, requiring assumptions that may often be too strong in applications. In many applications, individuals to the left and right of the threshold differ in observed characteristics. Houses may be constructed in different ways across school attendance district boundaries. Firms may differ around a threshold that implies certain legal changes, etc. Accounting for these differences in covariates is important to reduce bias. In addition, accounting for covariates may also reduces variance. Finally, estimation of quantile treatment effects (QTE) is also considered.

Keywords: treatment effect; causal effect; complier; LATE; nonparametric regression; endogeneity (search for similar items in EconPapers)
JEL-codes: C13 C14 C21 (search for similar items in EconPapers)
Pages: 25 pages
Date: 2007-09
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (24)

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Related works:
Working Paper: Regression discontinuity design with covariates (2007)
Working Paper: Regression discontinuity design with covariates (2007)
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