Inverse probability-weighted estimators are widely used in applications where data are
missing due to nonresponse or censoring and in the estimation of causal effects from …

We consider inference about the causal effect of a treatment or exposure in the presence of
interference, ie, when one individual's treatment affects the outcome of another individual. In …

Weighting methods that adjust for observed covariates, such as inverse probability
weighting, are widely used for causal inference and estimation with incomplete outcome …

Inverse probability weighting (IPW) can deal with confounding in non randomized studies.
The inverse weights are probabilities of treatment assignment (propensity scores), estimated …

Inverse probability weighting estimation has been popularly used to consistently estimate
the average treatment effect. Its validity, however, is challenged by the presence of error …

Abstract Inverse-Probability-of-Treatment-Weighted (IPTW) estimators are becoming a
popular analysis tool in causal inference. It is well known that these estimators suffer from …

Inverse probability weighting (IPW) is a general tool in survey sampling and causal
inference, used in both Horvitz–Thompson estimators, which normalize by the sample size …

This article reviews inverse probability weighting methods and doubly robust estimation
methods for the analysis of incomplete data sets. We first consider methods for estimating a …

Consider estimating the mean of an outcome in the presence of missing data or estimating
population average treatment effects in causal inference. A doubly robust estimator remains …

Inverse probability weighting (IPW) and multiple imputation are two widely adopted
approaches dealing with missing data. The former models the selection probability, and the …