Marginal structural models (MSM) provide a powerful tool for estimating the causal effect of a treatment. These models, introduced by Robins (e.g. Robins (2000a), Robins (2000b), van der Laan and Robins (2002)), model the marginal distributions of treatment-specific counterfactual outcomes,...

A simulation study was conducted to compare estimates from a naïve estimator, using standard conditional regression, and an IPTW (Inverse Probability of Treatment Weighted) estimator, to true causal parameters for a given MSM (Marginal Structural Model). The study was extracted from a larger...

An important problem in epidemiology and medical research is the estimation of a causal effect of a treatment action at a single point in time on the mean of an outcome within a population defined by strata of some of the observed covariates. Marginal structural models (MSM) are models for...

The causal effect of a treatment on an outcome is generally mediated by several intermediate variables. Estimation of the component of the causal effect of a treatment that is mediated by a given intermediate variable (the indirect effect of the treatment), and the component that is not mediated...

Drawing inferences about the effects of exposures or treatments is a common challenge in many scientific fields. We propose two methods serving complementary purposes in causal inference. One can be used to estimate average causal effects, assuming ``no confounding" given measured covariates....