In this article we introduce the concept of inverse probability of treatment weighting (IPTW)
and describe how this method can be applied to adjust for measured confounding in …

The propensity score is defined as a subject's probability of treatment selection, conditional
on observed baseline covariates. Weighting subjects by the inverse probability of treatment …

Importance Randomized clinical trials of bariatric surgery have been limited in size, type of
surgical procedure, and follow-up duration. Objective To determine long-term glycemic …

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 …

A standard assumption for causal inference from observational data is that one has
measured a sufficiently rich set of covariates to ensure that within covariate strata, subjects …

Background: Coronavirus disease 2019 (COVID-19) can lead to acute respiratory distress
syndrome (ARDS) but it is unknown whether prone positioning improves outcomes in …

The propensity score is the probability of treatment assignment conditional on observed
baseline characteristics. The propensity score allows one to design and analyze an …

A growing literature documents the importance of family instability for child wellbeing. In this
article, we use longitudinal data from the Fragile Families and Child Wellbeing Study to …

Theory suggests that neighborhood effects depend not only on where individuals live today,
but also on where they lived in the past. Previous research, however, usually measures …

The method of inverse probability weighting (henceforth, weighting) can be used to adjust
for measured confounding and selection bias under the four assumptions of consistency …