Causal inference is a critical research topic across many domains, such as statistics,
computer science, education, public policy, and economics, for decades. Nowadays …

Background: Characterization of the epigenome is a primary interest for children's
environmental health researchers studying the environmental influences on human …

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 …

Many modern estimators require bootstrapping to calculate confidence intervals because
either no analytic standard error is available or the distribution of the parameter of interest is …

The study of new medical treatments, and sequences of treatments, is inextricably linked
with statistics. Without statistical estimation and inference, we are left with case studies and …

Purpose Lack of control for time‐varying exposures can lead to substantial bias in estimates
of treatment effects. The aim of this study is to provide an overview and guidance on some of …

In recent years, targeted minimum loss-based estimation methodology has been used to
develop estimators of parameters in longitudinal data structures (Gruber and van der Laan …

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

In this MiniReview, we provide general considerations for the planning and conduct of
pharmacoepidemiological studies of associations between drug use and cancer …

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 …