Interpreting Regression Discontinuity Designs with Multiple Cutoffs

@article{Cattaneo2016InterpretingRD,
  title={Interpreting Regression Discontinuity Designs with Multiple Cutoffs},
  author={M. D. Cattaneo and Rocı́o Titiunik and Gonzalo V{\'a}zquez-Bar{\'e} and Luke J. Keele},
  journal={The Journal of Politics},
  year={2016},
  volume={78},
  pages={1229 - 1248}
}
We consider a regression discontinuity (RD) design where the treatment is received if a score is above a cutoff, but the cutoff may vary for each unit in the sample instead of being equal for all units. This multi-cutoff regression discontinuity design is very common in empirical work, and researchers often normalize the score variable and use the zero cutoff on the normalized score for all observations to estimate a pooled RD treatment effect. We formally derive the form that this pooled… 
View on U Chicago Press
www-personal.umich.edu
143 Citations
Highly Influential Citations
6
Background Citations
49
Methods Citations
20
Results Citations
3

143 Citations

Citation Type

Citation Type

Regression Discontinuity Design with Many Thresholds
Numerous empirical studies employ regression discontinuity designs with multiple cutoffs and heterogeneous treatments. A common practice is to normalize all the cutoffs to zero and estimate one…
Uncovering Time-Specific Heterogeneity in Regression Discontinuity Designs
The literature that employs Regression Discontinuity Designs (RDD) typically stacks data across time periods and cutoff values. While practical, this procedure omits useful time heterogeneity. In…
Extrapolating Treatment Effects in Multi-Cutoff Regression Discontinuity Designs
TLDR
A new method for extrapolation of RD effects that relies on the presence of multiple cutoffs, and is therefore design-based, is introduced, employing an easy-to-interpret identifying assumption that mimics the idea of “common trends” in difference-in-differences designs.
Regression Discontinuity with Multiple Running Variables Allowing Partial Effects
In regression discontinuity (RD), a running variable (or “score”) crossing a cutoff determines a treatment that affects the mean-regression function. This paper generalizes this usual “one-score mean…
Regression discontinuity design with many thresholds
On Interpreting the Regression Discontinuity Design as a Local Experiment
We discuss the two most popular frameworks for identification, estimation and inference in regression discontinuity (RD) designs: the continuity-based framework, where the conditional expectations of…
Regression Discontinuity Designs With Sample Selection
This article extends the standard regression discontinuity (RD) design to allow for sample selection or missing outcomes. We deal with both treatment endogeneity and sample selection. Identification…
Testing treatment effect heterogeneity in regression discontinuity designs
Regression Discontinuity Design with Multiple Groups for Heterogeneous Causal Effect Estimation
We propose a new estimation method for heterogeneous causal effects which utilizes a regression discontinuity (RD) design for multiple datasets with different thresholds. The standard RD design is…
REGRESSION DISCONTINUITY DESIGNS : THEORY AND APPLICATIONS
We discuss the two most popular frameworks for identification, estimation and inference in regression discontinuity (RD) designs: the continuitybased framework, where the conditional expectations of…
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 36 REFERENCES
Analyzing Regression-Discontinuity Designs With Multiple Assignment Variables
In a traditional regression-discontinuity design (RDD), units are assigned to treatment on the basis of a cutoff score and a continuous assignment variable. The treatment effect is measured at a…
Randomization Inference in the Regression Discontinuity Design: An Application to Party Advantages in the U.S. Senate
Abstract 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…
Identifying the Effect of Changing the Policy Threshold in Regression Discontinuity Models
Abstract Regression discontinuity models are commonly used to nonparametrically identify and estimate a local average treatment effect (LATE).We show that the derivative of the treatment effect with…
A Practical Guide to Regression Discontinuity Designs in Political Science
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…
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.
Extending the regression-discontinuity approach to multiple assignment variables
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…
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…
Misunderstandings About the Regression Discontinuity Design in the Study of Close Elections
TLDR
It is found that evidence for sorting in US House elections is substantially weaker and highly dependent on estimation methods, and the extrapolation of RD estimates away from the threshold sacrifices the design's advantages.
Robust Nonparametric Confidence Intervals for Regression‐Discontinuity Designs
In the regression‐discontinuity (RD) design, units are assigned to treatment based on whether their value of an observed covariate exceeds a known cutoff. In this design, local polynomial estimators…
...
1
2
3
4
...