Readings in Targeted Maximum Likelihood Estimation

Mark J. van der Laan, University of California - Berkeley
Sherri Rose, Division of Biostatistics, University of California, Berkeley
Susan Gruber, UC Berkeley

Abstract

This is a compilation of current and past work on targeted maximum likelihood estimation. It features the original targeted maximum likelihood learning paper as well as chapters on super (machine) learning using cross validation, randomized controlled trials, realistic individualized treatment rules in observational studies, biomarker discovery, case-control studies, and time-to-event outcomes with censored data, among others. We hope this collection is helpful to the interested reader and stimulates additional research in this important area.