  The participants in randomized trials and other studies used for causal
inference are often not representative of the populations seen by clinical
decision-makers. To account for differences between populations, researchers
may consider standardizing results to a target population. We discuss several
different types of homogeneity conditions that are relevant for
standardization: Homogeneity of effect measures, homogeneity of counterfactual
outcome state transition parameters, and homogeneity of counterfactual
distributions. Each of these conditions can be used to show that a particular
standardization procedure will result in unbiased estimates of the effect in
the target population, given assumptions about the relevant scientific context.
We compare and contrast the homogeneity conditions, in particular their
implications for selection of covariates for standardization and their
implications for how to compute the standardized causal effect in the target
population. While some of the recently developed counterfactual approaches to
generalizability rely upon homogeneity conditions that avoid many of the
problems associated with traditional approaches, they often require adjustment
for a large (and possibly unfeasible) set of covariates.
