Matias D. Cattaneo
YOU?
Author Swipe
View article: Boundary Discontinuity Designs: Theory and Practice
Boundary Discontinuity Designs: Theory and Practice Open
We review the literature on boundary discontinuity (BD) designs, a powerful non-experimental research methodology that identifies causal effects by exploiting a thresholding treatment assignment rule based on a bivariate score and a bounda…
View article: Robust Inference for Convex Pairwise Difference Estimators
Robust Inference for Convex Pairwise Difference Estimators Open
This paper develops distribution theory and bootstrap-based inference methods for a broad class of convex pairwise difference estimators. These estimators minimize a kernel-weighted convex-in-parameter function over observation pairs that …
View article: The Honest Truth About Causal Trees: Accuracy Limits for Heterogeneous Treatment Effect Estimation
The Honest Truth About Causal Trees: Accuracy Limits for Heterogeneous Treatment Effect Estimation Open
Recursive decision trees have emerged as a leading methodology for heterogeneous causal treatment effect estimation and inference in experimental and observational settings. These procedures are fitted using the celebrated CART (Classifica…
View article: rd2d: Boundary Regression Discontinuity Designs
rd2d: Boundary Regression Discontinuity Designs Open
View article: rdhte: Heterogeneous Treatment Effects in Regression Discontinuity Designs
rdhte: Heterogeneous Treatment Effects in Regression Discontinuity Designs Open
View article: Treatment Effect Heterogeneity in Regression Discontinuity Designs
Treatment Effect Heterogeneity in Regression Discontinuity Designs Open
Empirical studies using Regression Discontinuity (RD) designs often explore heterogeneous treatment effects based on pretreatment covariates. However, the lack of formal statistical methods has led to the widespread use of ad hoc approache…
View article: lpcde: Estimation and Inference for Local Polynomial Conditional Density Estimators
lpcde: Estimation and Inference for Local Polynomial Conditional Density Estimators Open
View article: Robust Inference for the Direct Average Treatment Effect with Treatment Assignment Interference
Robust Inference for the Direct Average Treatment Effect with Treatment Assignment Interference Open
This paper develops methods for uncertainty quantification in causal inference settings with random network interference. We study the large-sample distributional properties of the classical difference-in-means Hajek treatment effect estim…
View article: Sharp Anti-Concentration Inequalities for Extremum Statistics via Copulas
Sharp Anti-Concentration Inequalities for Extremum Statistics via Copulas Open
We derive sharp upper and lower bounds for the pointwise concentration function of the maximum statistic of $d$ identically distributed real-valued random variables. Our first main result places no restrictions either on the common margina…
View article: How Memory in Optimization Algorithms Implicitly Modifies the Loss
How Memory in Optimization Algorithms Implicitly Modifies the Loss Open
In modern optimization methods used in deep learning, each update depends on the history of previous iterations, often referred to as memory, and this dependence decays fast as the iterates go further into the past. For example, gradient d…
View article: Continuity of the Distribution Function of the argmax of a Gaussian Process
Continuity of the Distribution Function of the argmax of a Gaussian Process Open
Certain extremum estimators have asymptotic distributions that are non-Gaussian, yet characterizable as the distribution of the $\argmax$ of a Gaussian process. This paper presents high-level sufficient conditions under which such asymptot…
View article: <b>scpi</b>: Uncertainty Quantification for Synthetic Control Methods
<b>scpi</b>: Uncertainty Quantification for Synthetic Control Methods Open
View article: Leveraging covariates in regression discontinuity designs
Leveraging covariates in regression discontinuity designs Open
It is common practice to incorporate additional covariates in empirical economics. In the context of regression discontinuity (RD) designs, covariate adjustment plays multiple roles, making it essential to understand its impact on analysis…
View article: On Rosenbaum’s rank-based matching estimator
On Rosenbaum’s rank-based matching estimator Open
Summary In two influential contributions, Rosenbaum (2005, 2020a) advocated for using the distances between componentwise ranks, instead of the original data values, to measure covariate similarity when constructing matching estimators of …
View article: Randomization Inference for Before-and-After Studies with Multiple Units: An Application to a Criminal Procedure Reform in Uruguay
Randomization Inference for Before-and-After Studies with Multiple Units: An Application to a Criminal Procedure Reform in Uruguay Open
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 parametric modeling assumptions. As an alternative, we develop a …
View article: Uniform Estimation and Inference for Nonparametric Partitioning-Based M-Estimators
Uniform Estimation and Inference for Nonparametric Partitioning-Based M-Estimators Open
This paper presents uniform estimation and inference theory for a large class of nonparametric partitioning-based M-estimators. The main theoretical results include: (i) uniform consistency for convex and non-convex objective functions; (i…
View article: Nonlinear Binscatter Methods
Nonlinear Binscatter Methods Open
Binned scatter plots are a powerful statistical tool for empirical work in the social, behavioral, and biomedical sciences. Available methods rely on a quantile-based partitioning estimator of the conditional mean regression function to pr…
View article: Nonlinear Binscatter Methods
Nonlinear Binscatter Methods Open
Binned scatter plots are a powerful statistical tool for empirical work in the social, behavioral, and biomedical sciences. Available methods rely on a quantile-based partitioning estimator of the conditional mean regression function to pr…
View article: Strong Approximations for Empirical Processes Indexed by Lipschitz Functions
Strong Approximations for Empirical Processes Indexed by Lipschitz Functions Open
This paper presents new uniform Gaussian strong approximations for empirical processes indexed by classes of functions based on $d$-variate random vectors ($d\geq1$). First, a uniform Gaussian strong approximation is established for genera…
View article: Protocols for Observational Studies: An Application to Regression Discontinuity Designs
Protocols for Observational Studies: An Application to Regression Discontinuity Designs Open
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 discuss his proposal and, inspired by his ideas, we develop a pr…
View article: Nonlinear Binscatter Methods
Nonlinear Binscatter Methods Open
View article: On Rosenbaum's Rank-based Matching Estimator
On Rosenbaum's Rank-based Matching Estimator Open
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 similarity when constructing matching estimators of average …
View article: Inference with Mondrian Random Forests
Inference with Mondrian Random Forests Open
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 Mondrian random forest, in which the underlying constituent trees…
View article: On the Implicit Bias of Adam
On the Implicit Bias of Adam Open
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 sizes implicitly regularize solutions because terms appear…
View article: Beta-sorted portfolios
Beta-sorted portfolios Open
Beta-sorted portfolios-portfolios comprised of assets with similar covariation to selected risk factors-are a popular tool in empirical finance to analyze models of (conditional) expected returns. Despite their widespread use, little is kn…
View article: Beta-Sorted Portfolios
Beta-Sorted Portfolios Open
Beta-sorted portfolios—portfolios comprised of assets with similar covariation to selected risk factors—are a popular tool in empirical finance to analyze models of (conditional) expected returns. Despite their widespread use, little is kn…
View article: Context-Dependent Heterogeneous Preferences: A Comment on Barseghyan and Molinari (2023)
Context-Dependent Heterogeneous Preferences: A Comment on Barseghyan and Molinari (2023) Open
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, allowing for unobserved heterogeneity in utility functions an…
View article: Bootstrap-Assisted Inference for Generalized Grenander-type Estimators
Bootstrap-Assisted Inference for Generalized Grenander-type Estimators Open
Westling and Carone (2020) proposed a framework for studying the large sample distributional properties of generalized Grenander-type estimators, a versatile class of nonparametric estimators of monotone functions. The limiting distributio…
View article: Covariate Adjustment in Regression Discontinuity Designs
Covariate Adjustment in Regression Discontinuity Designs Open
This chapter reviews the different roles of covariate adjustment in the regression discontinuity (RD) literature, and to offer methodological guidance for its correct use in applications. One of the most important roles of baseline covaria…
View article: A Guide to Regression Discontinuity Designs in Medical Applications
A Guide to Regression Discontinuity Designs in Medical Applications Open
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 within both the continuity-based framework and the local random…