Add to ORKGThis study uses Monte Carlo simulations to demonstrate that regression-discontinuity designs arrive at biased estimates when attributes related to outcomes predict heaping in the running variable. After showing that our usual diagnostics are poorly suited to identifying this type of problem, we provide alternatives, and then discuss the usefulness of different approaches to addressing the bias. We then consider these issues in multiple non-simulated environments
In this article, we introduce three commands to conduct robust data-driven statistical inference in ...
This handbook chapter gives an introduction to the sharp regression discontinuity design, covering i...
This paper proposes empirical likelihood based inference methods for causal effects identified from ...
The Regression Discontinuity (RD) design looks similar to the non-equivalent group design, which use...
The regression-discontinuity design (RD) is a powerful methodological alternative to the quasi-exper...
We use elections data in which a large number of ties in vote counts betweencandidates...
Since the late 90s, Regression Discontinuity (RD) designs have been widely used to estimate Local Av...
<p>This article proposes a fully nonparametric kernel method to account for observed covariates in r...
We use elections data in which a large number of ties in vote counts between candidates are resolved...
<p>This article extends the standard regression discontinuity (RD) design to allow for sample select...
Regression discontinuity (RD) designs enable researchers to estimate causal effects using observatio...
This thesis studies regression discontinuity designs with the use of additional covariates for estim...
This paper reviews the literature on whether regression-discontinuity studies reproduce the results ...
Reichardt claim to "set the record straight regarding the RD design. " Instead, they defen...
We consider a regression discontinuity design where the treatment is received if a score is above a ...
In this article, we introduce three commands to conduct robust data-driven statistical inference in ...
This handbook chapter gives an introduction to the sharp regression discontinuity design, covering i...
This paper proposes empirical likelihood based inference methods for causal effects identified from ...
The Regression Discontinuity (RD) design looks similar to the non-equivalent group design, which use...
The regression-discontinuity design (RD) is a powerful methodological alternative to the quasi-exper...
We use elections data in which a large number of ties in vote counts betweencandidates...
Since the late 90s, Regression Discontinuity (RD) designs have been widely used to estimate Local Av...
<p>This article proposes a fully nonparametric kernel method to account for observed covariates in r...
We use elections data in which a large number of ties in vote counts between candidates are resolved...
<p>This article extends the standard regression discontinuity (RD) design to allow for sample select...
Regression discontinuity (RD) designs enable researchers to estimate causal effects using observatio...
This thesis studies regression discontinuity designs with the use of additional covariates for estim...
This paper reviews the literature on whether regression-discontinuity studies reproduce the results ...
Reichardt claim to "set the record straight regarding the RD design. " Instead, they defen...
We consider a regression discontinuity design where the treatment is received if a score is above a ...
In this article, we introduce three commands to conduct robust data-driven statistical inference in ...
This handbook chapter gives an introduction to the sharp regression discontinuity design, covering i...
This paper proposes empirical likelihood based inference methods for causal effects identified from ...