• Corpus ID: 215828583

Noise-Induced Randomization in Regression Discontinuity Designs

@article{Eckles2020NoiseInducedRI,
  title={Noise-Induced Randomization in Regression Discontinuity Designs},
  author={Dean Eckles and Nikolaos Ignatiadis and Stefan Wager and Han Wu},
  journal={arXiv: Methodology},
  year={2020}
}
Regression discontinuity designs are used to estimate causal effects in settings where treatment is determined by whether an observed running variable crosses a pre-specified threshold. While the resulting sampling design is sometimes described as akin to a locally randomized experiment in a neighborhood of the threshold, standard formal analyses do not make reference to probabilistic treatment assignment and instead identify treatment effects via continuity arguments. Here we propose a new… 

Figures and Tables from this paper

When Can We Ignore Measurement Error in the Running Variable?
In many empirical applications of regression discontinuity designs, the running variable used by the administrator to assign treatment is only observed with error. We show that, provided the observed
Confidence Intervals for Nonparametric Empirical Bayes Analysis
In an empirical Bayes analysis, we use data from repeated sampling to imitate inferences made by an oracle Bayesian with extensive knowledge of the data-generating distribution. Existing results

References

SHOWING 1-10 OF 101 REFERENCES
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
Complex Discontinuity Designs Using Covariates
TLDR
A new framework and methods for complex discontinuity designs that encompasses multiple treatment rules that may be determined by multiple running variables, each with many cutoffs, that possibly lead to the same treatment.
Optimized Regression Discontinuity Designs
TLDR
This work proposes an alternative method for estimation and statistical inference in regression discontinuity designs that uses numerical convex optimization to directly obtain the finite-sample-minimax linear estimator for the regression discontinuit parameter, subject to bounds on the second derivative of the conditional response function.
Random Measurement Error Does Not Bias the Treatment Effect Estimate in the Regression-Discontinuity Design
This article examines the regression-discontinuity (RD) design when there is random measure ment error and a treatment interaction effect. Two simulation issues -the specification of the
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
The Effect of Measurement Error in the Sharp Regression Discontinuity Design
This paper develops a nonparametric analysis for the sharp regression discontinuity (RD) design in which the continuous forcing variable may contain measurement error. We show that if the observable
A Correction for Regression Discontinuity Designs With Group-Specific Mismeasurement of the Running Variable
Abstract When the running variable in a regression discontinuity (RD) design is measured with error, identification of the local average treatment effect of interest will typically fail. While the
Evaluating the Causal Effect of University Grants on Student Dropout: Evidence from a Regression Discontinuity Design Using Principal Stratification
Regression discontinuity (RD) designs are often interpreted as local randomized experiments: a RD design can be considered as a randomized experiment for units with a realized value of a so-called
A Permutation Test for the Regression Kink Design
ABSTRACT The regression kink (RK) design is an increasingly popular empirical method for estimating causal effects of policies, such as the effect of unemployment benefits on unemployment duration.
The Devil is in the Tails: Regression Discontinuity Design with Measurement Error in the Assignment Variable
Identification in a regression discontinuity (RD) design hinges on the discontinuity in the probability of treatment when a covariate (assignment variable) exceeds a known threshold. If the
...
...