537 citations · 716 across the 19 of their papers we have counts for
22 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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
Regression Discontinuity Designs
Matias D. Cattaneo, Rocio Titiunik
The Regression Discontinuity (RD) design is one of the most widely used non-experimental methods for causal inference and program evaluation. Over the last two decades, statistical…
Natural Experiments
Rocio Titiunik
The term natural experiment is used inconsistently. In one interpretation, it refers to an experiment where a treatment is randomly assigned by someone other than the researcher. I…
Analysis of Regression Discontinuity Designs with Multiple Cutoffs or Multiple Scores
Matias D. Cattaneo, Rocio Titiunik, Gonzalo Vazquez-Bare
We introduce the \texttt{Stata} (and \texttt{R}) package \texttt{rdmulti}, which includes three commands (\texttt{rdmc}, \texttt{rdmcplot}, \texttt{rdms}) for analyzing Regression…
Prediction Intervals for Synthetic Control Methods
Matias D. Cattaneo, Yingjie Feng, Rocio Titiunik
Uncertainty quantification is a fundamental problem in the analysis and interpretation of synthetic control (SC) methods. We develop conditional prediction intervals in the SC fram…
A Practical Introduction to Regression Discontinuity Designs: Foundations
Matias D. Cattaneo, Nicolas Idrobo, Rocio Titiunik
In this Element and its accompanying Element, Matias D. Cattaneo, Nicolas Idrobo, and Rocio Titiunik provide an accessible and practical guide for the analysis and interpretation o…
The Regression Discontinuity Design
Matias D. Cattaneo, Rocio Titiunik, Gonzalo Vazquez-Bare
This handbook chapter gives an introduction to the sharp regression discontinuity design, covering identification, estimation, inference, and falsification methods.
Regression Discontinuity Designs Using Covariates
Sebastian Calonico, Matias D. Cattaneo, Max H. Farrell +1
We study regression discontinuity designs when covariates are included in the estimation. We examine local polynomial estimators that include discrete or continuous covariates in a…
Extrapolating Treatment Effects in Multi-Cutoff Regression Discontinuity Designs
Matias D. Cattaneo, Luke Keele, Rocio Titiunik +1
In non-experimental settings, the Regression Discontinuity (RD) design is one of the most credible identification strategies for program evaluation and causal inference. However, R…