• Corpus ID: 32262149

A Practical Guide to Regression Discontinuity Designs in Political Science

@inproceedings{Skovron2015APG,
  title={A Practical Guide to Regression Discontinuity Designs in Political Science},
  author={Christopher Skovron and Rocı́o Titiunik},
  year={2015}
}
We provide a practical guide to the analysis and interpretation of the regression discontinuity (RD) design, an empirical strategy that political scientists are increasingly employing to estimate causal effects with observational data. The defining feature of the RD design is that a treatment is assigned based on whether the value of a score exceeds a known cutoff. We review core conceptual issues, discussing the differences and similarities between RD designs and randomized controlled… 
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  • Lixiong Yang
  • Economics
    Studies in Nonlinear Dynamics & Econometrics
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Abstract This paper extends Regression discontinuity designs with unknown discontinuity points developed by (Porter, J., and P. Yu. 2015. “Regression Discontinuity Designs with Unknown Discontinuity
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References

SHOWING 1-10 OF 30 REFERENCES
Comparing Inference Approaches for RD Designs: a Reexamination of the Effect of Head Start on Child Mortality.
TLDR
Applying all these methods to the Head Start data, it is found that the original RD treatment effect reported in the literature is quite stable and robust, an empirical finding that enhances the credibility of the original result.
Geographic Boundaries as Regression Discontinuities
Political scientists often turn to natural experiments to draw causal inferences with observational data. Recently, the regression discontinuity design (RD) has become a popular type of natural
STRENGTHENING THE REGRESSION DISCONTINUITY DESIGN USING ADDITIONAL DESIGN ELEMENTS: A WITHIN‐STUDY COMPARISON
TLDR
The results show that adding the pretest makes functional form assumptions more transparent and improves on the standard RDD in multiple ways that bring causal estimates and their standard errors closer to those of an RCT, not just at the cutoff, but also away from it.
IDENTIFICATION AND ESTIMATION OF TREATMENT EFFECTS WITH A REGRESSION-DISCONTINUITY DESIGN
Ž. THE REGRESSION DISCONTINUITY RD data design is a quasi-experimental design with the defining characteristic that the probability of receiving treatment changes discontinuously as a function of one
Regression Discontinuity Designs in Economics
This paper provides an introduction and “user guide” to Regression Discontinuity (RD) designs for empirical researchers. It presents the basic theory behind the research design, details when RD is
Strengthening the Experimenter’s Toolbox: Statistical Estimation of Internal Validity
TLDR
It is argued that the standard normal theory statistical paradigm used in political science fails to meet the needs of experimenters and an alternative approach to statistical inference based on randomization of the treatment is outlined, and a brief overview of its technical details is offered.
Randomized experiments from non-random selection in U.S. House elections
Abstract This paper establishes the relatively weak conditions under which causal inferences from a regression–discontinuity (RD) analysis can be as credible as those from a randomized experiment,
On the Validity of the Regression Discontinuity Design for Estimating Electoral Effects: New Evidence from over 40,000 Close Races
The regression discontinuity (RD) design is a valuable tool for identifying electoral effects, but this design is only effective when relevant actors do not have precise control over election
Wanna Get Away? Regression Discontinuity Estimation of Exam School Effects Away From the Cutoff
In regression discontinuity (RD) studies exploiting an award or admissions cutoff, causal effects are nonparametrically identified for those near the cutoff. The effect of treatment on inframarginal
Elections and the Regression Discontinuity Design: Lessons from Close U.S. House Races, 1942–2008
Following David Lee's pioneering work, numerous scholars have applied the regression discontinuity (RD) design to popular elections. Contrary to the assumptions of RD, however, we show that bare
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