• Corpus ID: 235593221

Heterogeneous Treatment Effects in Regression Discontinuity Designs

@inproceedings{Reguly2021HeterogeneousTE,
  title={Heterogeneous Treatment Effects in Regression Discontinuity Designs},
  author={'Agoston Reguly},
  year={2021}
}
The paper proposes a causal supervised machine learning algorithm to uncover treatment effect heterogeneity in classical regression discontinuity (RD) designs. Extending Athey and Imbens (2016), I develop a criterion for building an honest “regression discontinuity tree”, where each leaf of the tree contains the RD estimate of a treatment (assigned by a common cutoff rule) conditional on the values of some pre-treatment covariates. It is a priori unknown which covariates are relevant for… 
Covariate Adjustment in Regression Discontinuity Designs
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

References

SHOWING 1-10 OF 44 REFERENCES
Double Machine Learning Based Program Evaluation under Unconfoundedness
  • M. Knaus
  • Economics, Computer Science
  • 2020
TLDR
This paper consolidates recent methodological developments based on Double Machine Learning with a focus on program evaluation under unconfoundedness and finds evidence that estimates of individualized heterogeneous effects can become unstable.
Recursive partitioning for heterogeneous causal effects
  • S. Athey, G. Imbens
  • Mathematics, Economics
    Proceedings of the National Academy of Sciences
  • 2016
TLDR
This paper provides a data-driven approach to partition the data into subpopulations that differ in the magnitude of their treatment effects, and proposes an “honest” approach to estimation, whereby one sample is used to construct the partition and another to estimate treatment effects for each subpopulation.
nuity designs using covariates
  • Review of Economics and Statistics,
  • 2014
Going to a Better School: Effects and Behavioral Responses
This paper: i) estimates the effect that going to a better school has on students' academic achievement, and ii) explores whether this intervention induces behavioral responses on the part of
Regression Discontinuity Designs in Economics
This paper provides an introduction and “user guide” to Regression Discontinuity (RD) designs for empirical researchers. It presents the basic theory behind the research design, details when RD is
Over-identified regression discon
  • 2017
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
Regression discontinuity designs: A guide to practice
  • Journal of Econometrics, 142(2):615–635.
  • 2008
Randomized experiments from non-random selection in U.S. House elections
Abstract This paper establishes the relatively weak conditions under which causal inferences from a regression–discontinuity (RD) analysis can be as credible as those from a randomized experiment,
Local Linear Forests
TLDR
A central limit theorem valid under regularity conditions on the forest and smoothness constraints is proved, a computationally efficient construction for confidence intervals is proposed, and a causal inference application is discussed.
...
1
2
3
4
5
...