338 citations · 769 across the 16 of their papers we have counts for
17 papers
Robust A/B Decisions
Max H. Farrell, Malika Korganbekova, Sanjog Misra
A/B tests are standard in firm decision making. In the standard pipeline, experimental data is converted to a deployment decision by applying a t-test of the difference in means (t…
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…
Nonlinear Binscatter Methods
Matias D. Cattaneo, Richard K. Crump, Max H. Farrell +1
Binscatters are a powerful tool for empirical work in the social, behavioral, and biomedical sciences. Available tools rely on least squares estimation of the conditional mean. We…
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…
lspartition: Partitioning-Based Least Squares Regression
Matias D. Cattaneo, Max H. Farrell, Yingjie Feng
Nonparametric partitioning-based least squares regression is an important tool in empirical work. Common examples include regressions based on splines, wavelets, and piecewise poly…
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…
Binscatter Regressions
Matias D. Cattaneo, Richard K. Crump, Max H. Farrell +1
We introduce the package Binsreg, which implements the binscatter methods developed by Cattaneo, Crump, Farrell, and Feng (2024b,a). The package includes seven commands: binsreg, b…
On Binscatter
Matias D. Cattaneo, Richard K. Crump, Max H. Farrell +1
Binscatter is a popular method for visualizing bivariate relationships and conducting informal specification testing. We study the properties of this method formally and develop en…
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…
Deep Neural Networks for Estimation and Inference
Max H. Farrell, Tengyuan Liang, Sanjog Misra
We study deep neural networks and their use in semiparametric inference. We establish novel rates of convergence for deep feedforward neural nets. Our new rates are sufficiently fa…
Characteristic-Sorted Portfolios: Estimation and Inference
Matias D. Cattaneo, Richard K. Crump, Max H. Farrell +1
Portfolio sorting is ubiquitous in the empirical finance literature, where it has been widely used to identify pricing anomalies. Despite its popularity, little attention has been…
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…
Large Sample Properties of Partitioning-Based Series Estimators
Matias D. Cattaneo, Max H. Farrell, Yingjie Feng
We present large sample results for partitioning-based least squares nonparametric regression, a popular method for approximating conditional expectation functions in statistics, e…
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…
Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations
Max H. Farrell
This paper concerns robust inference on average treatment effects following model selection. In the selection on observables framework, we show how to construct confidence interval…