• Corpus ID: 235593221

Heterogeneous Treatment Effects in Regression Discontinuity Designs

  title={Heterogeneous Treatment Effects in Regression Discontinuity Designs},
  author={'Agoston Reguly},
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


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  • M. Knaus
  • Economics, Computer Science
  • 2020
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
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
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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
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  • Journal of Econometrics, 142(2):615–635.
  • 2008
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