• Publications
  • Influence
Regression Discontinuity Designs Using Covariates
Abstract We study regression discontinuity designs when covariates are included in the estimation. We examine local polynomial estimators that include discrete or continuous covariates in an additive
Rdrobust: Software for Regression-discontinuity Designs
A major upgrade to the Stata (and R) rdrobust package, which provides a wide array of estimation, inference, and falsification methods for the analysis and interpretation of regression-discontinuity designs, has superior performance because of several numerical and implementation improvements.
On the Effect of Bias Estimation on Coverage Accuracy in Nonparametric Inference
ABSTRACT 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 quite
Deep Neural Networks for Estimation and Inference
This work establishes novel rates of convergence for deep feedforward neural nets and establishes nonasymptotic bounds for these deep nets for a general class of nonparametric regression-type loss functions, which includes as special cases least squares, logistic regression, and other generalized linear models.
Prevalence of Urinary Tract Infection in Childhood: A Meta-Analysis
A meta-analysis to determine the pooled prevalence of urinary tract infection in children by age, gender, race, and circumcision status found uncircumcised male infants less than 3 months of age and females less than 12 months ofAge had the highest baseline prevalence of UTI.
Optimal bandwidth choice for robust bias-corrected inference in regression discontinuity designs
This work establishes valid coverage error expansions for RBC confidence interval estimators and establishes that RBC inference yields higher-order refinements (relative to traditional undersmoothing) in the context of RD designs.
Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations
This paper concerns robust inference on average treatment effects following model selection. Under selection on observables, we construct confidence intervals using a doubly-robust estimator that are
Deep Neural Networks for Estimation and Inference: Application to Causal Effects and Other Semiparametric Estimands
This work provides new rates of convergence for deep feedforward neural nets and obtains valid semiparametric inference, and proves valid inference after first-step estimation with deep learning, a result new to the literature.
Coverage Error Optimal Confidence Intervals
The framework employs the "check" function to quantify coverage error loss, which allows researchers to incorporate their preference in terms of over- and under-coverage, where confidence intervals attaining the best-possible uniform coverage error are minimax optimal.