Kaufman, Jay S. (2010). Marginalia: Comparing Adjusted Effect Measures. Epidemiology, 21(4)
It has long been known that conditioning on the determinants of exposure can reduce confounding bias when estimating causal effects.1 This insight led to the development of propensity score methods 2 and inverse-probability-of-treatment weighting.3 The latter technique is used for the estimation of marginal structural models, where the word “marginal” in this name refers to the potential outcomes Y | SET(X = x) for exposure X, since this model estimates the marginal distribution of these (counterfactual) quantities. Inverse probability of treatment has also been shown to be equivalent to standardization for point-time treatment models.4,5 Confounding has traditionally been combated with a variety of techniques in epidemiologic research, and some important distinctions in the interpretations generated by these techniques are often overlooked in practice. In this brief comment, I note that effect measures adjusted simply via inverse probability of treatment weights have a marginal interpretation with respect to the covariates, and in this sense the word “marginal” has nothing to do with the first word in “marginal structural models.” Moreover, stabilization of the weights in a marginal structural model can change this interpretation from marginal to conditional—a potentially important consequence that appears to have not yet been widely discussed in published work. This can have implications for interpretation of effect estimates and for comparison of adjusted effect measures across different models.
Kaufman, Jay S.