Regression discontinuity designs (RDDs) have become one of the most
widely-used quasi-experimental tools for causal inference. A crucial assumption
on which they rely is that the running variable cannot be manipulated -- an
assumption frequently violated in practice, jeopardizing point identification.
In this paper, we introduce a novel method that provide partial identification
bounds on the causal parameter of interest in sharp and fuzzy RDDs. The method
first estimates the number of manipulators in the sample using a log-concavity
assumption on the un-manipulated density of the running variable. It then
derives best- and worst-case bounds when we delete that number of points from
the data, along with fast computational methods to obtain them. We apply this
procedure to a dataset of blood donations from the Abu Dhabi blood bank to
obtain the causal effect of donor deferral on future volunteering behavior. We
find that, despite significant manipulation in the data, we are able to detect
causal effects where traditional methods, such as donut-hole RDDs, fail.
Description
Optimized Partial Identification Bounds for Regression Discontinuity Designs with Manipulation
%0 Journal Article
%1 rosenman2019optimized
%A Rosenman, Evan
%A Rajkumar, Karthik
%A Gauriot, Romain
%A Slonim, Robert
%D 2019
%K Identification partial
%T Optimized Partial Identification Bounds for Regression Discontinuity
Designs with Manipulation
%U http://arxiv.org/abs/1910.02170
%X Regression discontinuity designs (RDDs) have become one of the most
widely-used quasi-experimental tools for causal inference. A crucial assumption
on which they rely is that the running variable cannot be manipulated -- an
assumption frequently violated in practice, jeopardizing point identification.
In this paper, we introduce a novel method that provide partial identification
bounds on the causal parameter of interest in sharp and fuzzy RDDs. The method
first estimates the number of manipulators in the sample using a log-concavity
assumption on the un-manipulated density of the running variable. It then
derives best- and worst-case bounds when we delete that number of points from
the data, along with fast computational methods to obtain them. We apply this
procedure to a dataset of blood donations from the Abu Dhabi blood bank to
obtain the causal effect of donor deferral on future volunteering behavior. We
find that, despite significant manipulation in the data, we are able to detect
causal effects where traditional methods, such as donut-hole RDDs, fail.
@article{rosenman2019optimized,
abstract = {Regression discontinuity designs (RDDs) have become one of the most
widely-used quasi-experimental tools for causal inference. A crucial assumption
on which they rely is that the running variable cannot be manipulated -- an
assumption frequently violated in practice, jeopardizing point identification.
In this paper, we introduce a novel method that provide partial identification
bounds on the causal parameter of interest in sharp and fuzzy RDDs. The method
first estimates the number of manipulators in the sample using a log-concavity
assumption on the un-manipulated density of the running variable. It then
derives best- and worst-case bounds when we delete that number of points from
the data, along with fast computational methods to obtain them. We apply this
procedure to a dataset of blood donations from the Abu Dhabi blood bank to
obtain the causal effect of donor deferral on future volunteering behavior. We
find that, despite significant manipulation in the data, we are able to detect
causal effects where traditional methods, such as donut-hole RDDs, fail.},
added-at = {2022-06-14T14:11:26.000+0200},
author = {Rosenman, Evan and Rajkumar, Karthik and Gauriot, Romain and Slonim, Robert},
biburl = {https://www.bibsonomy.org/bibtex/2aa6c9cf354491d99937098232ad78d77/vgnrunner},
description = {Optimized Partial Identification Bounds for Regression Discontinuity Designs with Manipulation},
interhash = {b0d2160f2ea9be4bbda721e169d23bc5},
intrahash = {aa6c9cf354491d99937098232ad78d77},
keywords = {Identification partial},
note = {cite arxiv:1910.02170},
timestamp = {2022-07-01T13:38:39.000+0200},
title = {Optimized Partial Identification Bounds for Regression Discontinuity
Designs with Manipulation},
url = {http://arxiv.org/abs/1910.02170},
year = 2019
}