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Eric B. Laber
Researcher at North Carolina State University
Publications - 119
Citations - 4112
Eric B. Laber is an academic researcher from North Carolina State University. The author has contributed to research in topics: Dynamic treatment regime & Estimator. The author has an hindex of 30, co-authored 107 publications receiving 3171 citations. Previous affiliations of Eric B. Laber include Duke University & John Wiley & Sons.
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A Robust Method for Estimating Optimal Treatment Regimes
TL;DR: A doubly robust augmented inverse probability weighted estimator is used for finding the optimal regime within a class of regimes by finding the regime that optimizes an estimator of overall population mean outcome.
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Estimating optimal treatment regimes from a classification perspective
TL;DR: This work proposes a novel and general framework that transforms the problem of estimating an optimal treatment regime into a classification problem wherein the optimal classifier corresponds to the optimalreatment regime.
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New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes
TL;DR: Two new statistical learning methods for estimating the optimal DTR are introduced, termed backward outcome weighted learning (BOWL) and simultaneous outcome weightedlearning (SOWL), and it is proved that the resulting rules are consistent, and provide finite sample bounds for the errors using the estimated rules.
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The ecology of microscopic life in household dust
Albert Barberán,Robert R. Dunn,Robert R. Dunn,Brian J. Reich,Krishna Pacifici,Eric B. Laber,Holly L. Menninger,James M. Morton,Jessica B. Henley,Jonathan W. Leff,Shelly L. Miller,Noah Fierer +11 more
TL;DR: Investigation of fungal and bacterial communities found in settled dust collected from inside and outside approximately 1200 homes located across the continental US, homes that represent a broad range of home designs and span many climatic zones found that who you live with determines what bacteria are found inside your home.
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Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions
TL;DR: This work proposes an alternative to Q- and A-learning that maximizes a doubly robust augmented inverse probability weighted estimator for population mean outcome over a restricted class of regimes.