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Rdrobust: Software for Regression-discontinuity Designs
We describe 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 ofExpand
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Regression Discontinuity Designs Using Covariates
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 additiveExpand
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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 quiteExpand
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Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations
This paper concerns robust inference on average treatment effects following model selection. In the selection on observables framework, we show how to construct confidence intervals based on aExpand
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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 areExpand
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Optimal Bandwidth Choice for Robust Bias Corrected Inference in Regression Discontinuity Designs
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 bandwidth yieldsExpand
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Prevalence of Urinary Tract Infection in Childhood: A Meta-Analysis
Background: Knowledge of baseline risk of urinary tract infection can help clinicians make informed diagnostic and therapeutic decisions. We conducted a meta-analysis to determine the pooledExpand
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Mapping FACT-P and EORTC QLQ-C30 to patient health status measured by EQ-5D in metastatic hormone-refractory prostate cancer patients.
OBJECTIVES To construct and validate a prediction model of preference-adjusted health status (EQ-5D) for metastatic hormone-refractory prostate cancer (HRPCA) patients using cancer-specificExpand
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Deep Neural Networks for Estimation and Inference: Application to Causal Effects and Other Semiparametric Estimands
TLDR
We study deep neural networks and their use in semiparametric inference. Expand
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Deep Neural Networks for Estimation and Inference
TLDR
We study deep neural networks and their use in semiparametric inference. Expand
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