21 citations · 36 across the 7 of their papers we have counts for
7 papers
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…
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…
nprobust: Nonparametric Kernel-Based Estimation and Robust Bias-Corrected Inference
Sebastian Calonico, Matias D. Cattaneo, Max H. Farrell
Nonparametric kernel density and local polynomial regression estimators are very popular in Statistics, Economics, and many other disciplines. They are routinely employed in applie…
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…
Optimal Bandwidth Choice for Robust Bias Corrected Inference in Regression Discontinuity Designs
Sebastian Calonico, Matias D. Cattaneo, Max H. Farrell
Modern empirical work in Regression Discontinuity (RD) designs often employs local polynomial estimation and inference with a mean square error (MSE) optimal bandwidth choice. This…
Coverage Error Optimal Confidence Intervals for Local Polynomial Regression
Sebastian Calonico, Matias D. Cattaneo, Max H. Farrell
This paper studies higher-order inference properties of nonparametric local polynomial regression methods under random sampling. We prove Edgeworth expansions for statistics an…
On the Effect of Bias Estimation on Coverage Accuracy in Nonparametric Inference
Sebastian Calonico, Matias D. Cattaneo, Max H. Farrell
Nonparametric methods play a central role in modern empirical work. While they provide inference procedures that are more robust to parametric misspecification bias, they may be qu…