1 citations · 1 across the 9 of their papers we have counts for
11 papers
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…
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…
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…
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…
Leveraging Covariates in Regression Discontinuity Designs
Matias D. Cattaneo, Filippo Palomba
It is common practice to incorporate additional covariates in empirical economics. In the context of Regression Discontinuity (RD) designs, covariate adjustment plays multiple role…
rdhte: Conditional Average Treatment Effects in RD Designs
Sebastian Calonico, Matias D. Cattaneo, Max H. Farrell +2
Understanding causal heterogeneous treatment effects based on pretreatment covariates is a crucial aspect of empirical work. Building on Calonico, Cattaneo, Farrell, Palomba, and T…
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: 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…
Robust Inference for the Direct Average Treatment Effect with Treatment Assignment Interference
Matias D. Cattaneo, Yihan He, Ruiqi Rae Yu
This paper develops methods for uncertainty quantification in causal inference settings with random network interference. We study the large-sample distributional properties of the…
Sharp Anti-Concentration Inequalities for Extremum Statistics via Copulas
Matias D. Cattaneo, Ricardo P. Masini, William G. Underwood
We derive sharp upper and lower bounds for the pointwise concentration function of the maximum statistic of identically distributed real-valued random variables. Our first main…
On the Pointwise Behavior of Recursive Partitioning and Its Implications for Heterogeneous Causal Effect Estimation
Matias D. Cattaneo, Jason M. Klusowski, Peter M. Tian
Decision tree learning is increasingly being used for pointwise inference. Important applications include causal heterogenous treatment effects and dynamic policy decisions, as wel…