the absence of death up till time $t$ [@chaix]. This is arguably not a sensible population for inference, at least from a policy and public health perspective.

A more refined approach would be to compare the difference in the effect of negative wealth shock on cognitive ability among subjects who would have survived whether they experienced a negative wealth shock or not. This approach is consistent with the potential outcomes approach of [@neyman] and [@rubin_po], which defines causal effects as the within-subject difference of an outcome at a particular time under different exposure or treatment regimen, averaged over the population. This idea is not new [@elliott_bio] and can be viewed as a specific example of the principal stratification (PS) method discussed in [@frangakis]. Our innovation here is to embed this in a longitudinal setting where confounding by indication is present. We view this as a large missing data problem where survival status and, among survivors, unobserved outcomes under a given treatment pattern, are imputed. We extend the method proposed in Example 3 of [@elliott], which provides a Bayesian MSM approach to compare two treatments at two time points. This approach was further extended by PENCOMP in [@zhou_t] which, like augmented 