10 papers
Accuracy Limits of Causal Trees for Individualized Treatment Effects
Matias D. Cattaneo, Jason M. Klusowski, Ruiqi Rae Yu
Recursive decision trees are widely used to estimate heterogeneous causal treatment effects in experimental and observational studies. These methods are typically implemented using…
rd2d: Causal Inference in Boundary Discontinuity Designs
Matias D. Cattaneo, Rocio Titiunik, Ruiqi Rae Yu
Boundary Discontinuity (BD) designs are used in empirical research to learn about causal treatment effects along a continuous assignment boundary defined by a bivariate score. Thes…
Estimation and Inference in Boundary Discontinuity Designs: Distance-Based Methods
Matias D. Cattaneo, Rocio Titiunik, Ruiqi Rae Yu
We study nonparametric distance-based (isotropic) local polynomial methods for estimating the boundary average treatment effect curve, a causal functional that captures treatment e…
Estimation and Inference in Boundary Discontinuity Designs: Location-Based Methods
Matias D. Cattaneo, Rocio Titiunik, Ruiqi Rae Yu
Boundary discontinuity designs are used to learn about causal treatment effects along a continuous assignment boundary that splits units into control and treatment groups according…
Boundary Discontinuity Designs: Theory and Practice
Matias D. Cattaneo, Rocio Titiunik, Ruiqi Rae Yu
The boundary discontinuity (BD) design is a non-experimental method for identifying causal effects that exploits a thresholding rule based on a bivariate score and a boundary curve…
The Regression Discontinuity Design in Medical Science
Matias D. Cattaneo, Rocio Titiunik
This article provides an introduction to the Regression Discontinuity (RD) design, and its application to empirical research in the medical sciences. While the main focus of this a…