Background Obtaining unbiased causal estimates from longitudinal observational data can
be difficult due to exposure-affected time-varying confounding. The past decade has seen …

This paper addresses the use of neural networks for the estimation of treatment effects from
observational data. Generally, estimation proceeds in two stages. First, we fit models for the …

This book builds on and is a sequel to our book Targeted Learning: Causal Inference for
Observational and Experimental Studies (2011). Since the publication of this first book on …

Purpose The targeted maximum likelihood estimation (TMLE) statistical data analysis
framework integrates machine learning, statistical theory, and statistical inference to provide …

Doubly robust estimators are widely used to draw inference about the average effect of a
treatment. Such estimators are consistent for the effect of interest if either one of two …

Suppose we observe n independent and identically distributed observations of a finite
dimensional bounded random variable. This article is concerned with the construction of an …

Background While obesity confers an increased risk of death in the general population,
numerous studies have reported an association between obesity and improved survival …

Abstract The Highly-Adaptive least absolute shrinkage and selection operator (LASSO)
Targeted Minimum Loss Estimator (HAL-TMLE) is an efficient plug-in estimator of a pathwise …

In practice, imitation learning is preferred over pure reinforcement learning whenever it is
possible to design a teaching agent to provide expert supervision. However, we show that …

Consider estimating the G-formula for the counterfactual mean outcome under a given
treatment regime in a longitudinal study. Bang and Robins provided an estimator for this …