# Regression Discontinuity Design with Continuous Measurement Error in the Running Variable

@article{Davezies2017RegressionDD, title={Regression Discontinuity Design with Continuous Measurement Error in the Running Variable}, author={Laurent Davezies and Thomas Le Barbanchon}, journal={Labor: Public Policy \& Regulation eJournal}, year={2017} }

## 34 Citations

Estimating the Variance of Measurement Errors in Running Variables of Sharp Regression Discontinuity Designs

- Mathematics
- 2017

Treatment effect estimation through regression discontinuity designs faces a severe challenge when the running variable is measured with errors, as the errors smooth out the discontinuity that the…

A Correction for Regression Discontinuity Designs With Group-Specific Mismeasurement of the Running Variable

- MathematicsJournal of Business & Economic Statistics
- 2020

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…

Regression Discontinuity Designs with Nonclassical Measurement Error

- Economics, Mathematics
- 2015

This paper develops a nonparametric identification analysis in regression discontinuity (RD) designs where each observable may contain measurement error. Our analysis allows the measurement error to…

Regression Discontinuity Designs with Nonclassical Measurement Errors

- Economics, Mathematics
- 2015

This paper develops a nonparametric identification analysis in regression discontinuity (RD) designs where each observable may contain measurement error. Our analysis allows the measurement error to…

The Devil is in the Tails: Regression Discontinuity Design with Measurement Error in the Assignment Variable

- Economics, Mathematics
- 2016

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…

Noise-Induced Randomization in Regression Discontinuity Designs

- Economics, Mathematics
- 2020

A new approach to identification, estimation, and inference in regression discontinuity designs that exploits measurement error in the running variable is proposed and found to facilitate identification of both familiar estimands from the literature, as well as policy-relevant estimands that correspond to the effects of realistic changes to the existing treatment assignment rule.

Covariate Adjustment in Regression Discontinuity Designs

- Economics
- 2021

The Regression Discontinuity (RD) design is a widely used non-experimental method for causal inference and program evaluation. While its canonical formulation only requires a score and an outcome…

SYNTHETIC REGRESSION DISCONTINUITY ESTIMATING TREATMENT EFFECTS USING MACHINE LEARNING

- Economics
- 2019

In the standard regression discontinuity setting, treatment assignment is based on whether a unit’s observable score (running variable) crosses a known threshold. We propose a two-stage method to…

Regression Discontinuity Designs

- Mathematics
- 2017

The Regression Discontinuity (RD) design is one of the most widely used non-experimental methods for causal inference and program evaluation. Over the last two decades, statistical and econometric…

Regression Discontinuity Designs Based on Population Thresholds: Pitfalls and Solutions

- Economics
- 2016

In many countries, important features of municipal government (such as the electoral system, mayors' salaries, and the number of councillors) depend on whether the municipality is above or below…

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The Devil is in the Tails: Regression Discontinuity Design with Measurement Error in the Assignment Variable

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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…

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