Randomization Inference in the Regression Discontinuity Design: An Application to Party Advantages in the U.S. Senate

Journal of Causal Inference

article

Matias D. Cattaneo¹  Brigham R. Frandsen²  Rocío Titiunik³ 

[1] Department of Economics, University of Michigan;Department of Economics, Brigham Young University;Department of Political Science, University of Michigan

关键词: regression discontinuity;    randomization inference;    as-if randomization;    incumbency advantage;    U.S;    Senate;   

DOI  :  10.1515/jci-2013-0010

来源: De Gruyter

【 摘 要 】

In the Regression Discontinuity (RD) design, units are assigned a treatment based on whether their value of an observed covariate is above or below a fixed cutoff. Under the assumption that the distribution of potential confounders changes continuously around the cutoff, the discontinuous jump in the probability of treatment assignment can be used to identify the treatment effect. Although a recent strand of the RD literature advocates interpreting this design as a local randomized experiment, the standard approach to estimation and inference is based solely on continuity assumptions that do not justify this interpretation. In this article, we provide precise conditions in a randomization inference context under which this interpretation is directly justified and develop exact finite-sample inference procedures based on them. Our randomization inference framework is motivated by the observation that only a few observations might be available close enough to the threshold where local randomization is plausible, and hence standard large-sample procedures may be suspect. Our proposed methodology is intended as a complement and a robustness check to standard RD inference approaches. We illustrate our framework with a study of two measures of party-level advantage in U.S. Senate elections, where the number of close races is small and our framework is well suited for the empirical analysis.

【 授权许可】

CC BY