537 citations · 887 across the 42 of their papers we have counts for
50 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…
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…
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…
Strong Approximations for Empirical Processes Indexed by Lipschitz Functions
Matias D. Cattaneo, Ruiqi Rae Yu
This paper presents new uniform Gaussian strong approximations for empirical processes indexed by classes of functions based on -variate random vectors (). First, a unif…
Protocols for Observational Studies: An Application to Regression Discontinuity Designs
Matias D. Cattaneo, Rocio Titiunik
In his 2022 IMS Medallion Lecture delivered at the Joint Statistical Meetings, Prof. Dylan S. Small eloquently advocated for the use of protocols in observational studies. We discu…
On Rosenbaum's Rank-based Matching Estimator
Matias D. Cattaneo, Fang Han, Zhexiao Lin
In two influential contributions, Rosenbaum (2005, 2020) advocated for using the distances between component-wise ranks, instead of the original data values, to measure covariate s…
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…
On the Implicit Bias of Adam
Matias D. Cattaneo, Jason M. Klusowski, Boris Shigida
In previous literature, backward error analysis was used to find ordinary differential equations (ODEs) approximating the gradient descent trajectory. It was found that finite step…
Context-Dependent Heterogeneous Preferences: A Comment on Barseghyan and Molinari (2023)
Matias D. Cattaneo, Xinwei Ma, Yusufcan Masatlioglu
Barseghyan and Molinari (2023) give sufficient conditions for semi-nonparametric point identification of parameters of interest in a mixture model of decision-making under risk, al…
Bootstrap-Assisted Inference for Generalized Grenander-type Estimators
Matias D. Cattaneo, Michael Jansson, Kenichi Nagasawa
Westling and Carone (2020) proposed a framework for studying the large sample distributional properties of generalized Grenander-type estimators, a versatile class of nonparametric…
A Guide to Regression Discontinuity Designs in Medical Applications
Matias D. Cattaneo, Luke Keele, Rocio Titiunik
We present a practical guide for the analysis of regression discontinuity (RD) designs in biomedical contexts. We begin by introducing key concepts, assumptions, and estimands with…
A Practical Introduction to Regression Discontinuity Designs: Extensions
Matias D. Cattaneo, Nicolas Idrobo, Rocio Titiunik
This monograph, together with its accompanying first part Cattaneo, Idrobo and Titiunik (2020), collects and expands the instructional materials we prepared for more than shor…
Higher-order Refinements of Small Bandwidth Asymptotics for Density-Weighted Average Derivative Estimators
Matias D. Cattaneo, Max H. Farrell, Michael Jansson +1
The density weighted average derivative (DWAD) of a regression function is a canonical parameter of interest in economics. Classical first-order large sample distribution theory fo…
Convergence Rates of Oblique Regression Trees for Flexible Function Libraries
Matias D. Cattaneo, Rajita Chandak, Jason M. Klusowski
We develop a theoretical framework for the analysis of oblique decision trees, where the splits at each decision node occur at linear combinations of the covariates (as opposed to…
Uncertainty Quantification in Synthetic Controls with Staggered Treatment Adoption
Matias D. Cattaneo, Yingjie Feng, Filippo Palomba +1
We propose principled prediction intervals to quantify the uncertainty of a large class of synthetic control predictions (or estimators) in settings with staggered treatment adopti…
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…
Beta-Sorted Portfolios
Matias D. Cattaneo, Richard K. Crump, Weining Wang +1
Beta-sorted portfolios---portfolios comprised of assets with similar covariation with selected risk factors---are a popular tool in empirical finance to analyze models of (conditio…
lpcde: Estimation and Inference for Local Polynomial Conditional Density Estimators
Matias D. Cattaneo, Rajita Chandak, Michael Jansson +1
This paper discusses the R package lpcde, which stands for local polynomial conditional density estimation. It implements the kernel-based local polynomial smoothing methods introd…
Boundary Adaptive Local Polynomial Conditional Density Estimators
Matias D. Cattaneo, Rajita Chandak, Michael Jansson +1
We begin by introducing a class of conditional density estimators based on local polynomial techniques. The estimators are boundary adaptive and easy to implement. We then study th…
scpi: Uncertainty Quantification for Synthetic Control Methods
Matias D. Cattaneo, Yingjie Feng, Filippo Palomba +1
The synthetic control method offers a way to quantify the effect of an intervention using weighted averages of untreated units to approximate the counterfactual outcome that the tr…
Uniform Inference for Kernel Density Estimators with Dyadic Data
Matias D. Cattaneo, Yingjie Feng, William G. Underwood
Dyadic data is often encountered when quantities of interest are associated with the edges of a network. As such it plays an important role in statistics, econometrics and many oth…
Attention Overload
Matias D. Cattaneo, Paul Cheung, Xinwei Ma +1
We introduce an Attention Overload Model (AOM) in which alternatives compete for attention, so each alternative's consideration probability weakly decreases as the choice problem e…
Covariate Adjustment in Regression Discontinuity Designs
Matias D. Cattaneo, Luke Keele, Rocio Titiunik
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…