When estimating the average effect of a binary treatment (or exposure) on an outcome,
methods that incorporate propensity scores, the G‐formula, or targeted maximum likelihood …

The original" Seven Motifs" set forth a roadmap of essential methods for the field of scientific
computing, where a motif is an algorithmic method that captures a pattern of computation …

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 …

Background: The fundamental problem of causal inference is one of missing data, and
specifically of missing potential outcomes: if potential outcomes were fully observed, then …

The assumption of positivity or experimental treatment assignment requires that observed
treatment levels vary within confounder strata. This article discusses the positivity …

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 …

METHODS Data on demographic variables, socioeconomic factors, needs, and injury
recidivism from 2005 to 2014 were collected through our VIP database. Possible client …

Bias is a common problem inherent in recommender systems, which is entangled with users'
preferences and poses a great challenge to unbiased learning. For debiasing tasks, the …

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 …