15 papers
Robust Inference for Convex Pairwise Difference Estimators
Matias D. Cattaneo, Michael Jansson, Kenichi Nagasawa
This paper develops distribution theory and bootstrap-based inference methods for a broad class of convex pairwise difference estimators. These estimators minimize a kernel-weighte…
Inference with Mondrian Random Forests
Matias D. Cattaneo, Jason M. Klusowski, William G. Underwood
Random forests are popular methods for regression and classification analysis, and many different variants have been proposed in recent years. One interesting example is the Mondri…
Continuity of the Distribution Function of the argmax of a Gaussian Process
Matias D. Cattaneo, Gregory Fletcher Cox, Michael Jansson +1
Certain extremum estimators have asymptotic distributions that are non-Gaussian, yet characterizable as the distribution of the of a Gaussian process. This paper presents…
Randomization Inference for Before-and-After Studies with Multiple Units: An Application to a Criminal Procedure Reform in Uruguay
Matias D. Cattaneo, Carlos Diaz, Rocio Titiunik
Learning about the immediate causal effects of large-scale policy interventions poses a significant challenge for quasi-experimental methods that rely on long-term trends or parame…
Yurinskii's Coupling for Martingales
Matias D. Cattaneo, Ricardo P. Masini, William G. Underwood
Yurinskii's coupling is a popular theoretical tool for non-asymptotic distributional analysis in mathematical statistics and applied probability, offering a Gaussian strong approxi…
Treatment Effect Heterogeneity in Regression Discontinuity Designs
Sebastian Calonico, Matias D. Cattaneo, Max H. Farrell +2
Empirical studies using Regression Discontinuity (RD) designs often explore heterogeneous treatment effects based on pretreatment covariates, even though no formal statistical meth…