In 2004, Murray et al. reviewed methodological developments in the design and analysis of
group-randomized trials (GRTs). We have updated that review with developments in …

While machine learning (ML) methods have received a lot of attention in recent years, these
methods are primarily for prediction. Empirical researchers conducting policy evaluations …

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 …

Missing data affect nearly every discipline by complicating the statistical analysis of collected
data. But since the 1990s, there have been important developments in the statistical …

Most causal inference methods consider counterfactual variables under interventions that
set the exposure to a fixed value. With continuous or multi-valued treatments or exposures …

Targeted maximum likelihood estimation (TMLE) is a general approach for constructing an
efficient double-robust semi-parametric substitution estimator of a causal effect parameter or …

We consider estimation of the effect of a multiple time point intervention on an outcome of
interest, where the intervention nodes are subject to time-dependent confounding by …

Over the past decade, doubly robust estimators have been proposed for a variety of target
parameters in causal inference and missing data models. These are asymptotically …

Collaborative double robust targeted maximum likelihood estimators represent a
fundamental further advance over standard targeted maximum likelihood estimators of a …

We develop robust targeted maximum likelihood estimators (TMLEs) for transporting
intervention effects from one population to another. Specifically, we develop TMLEs for three …