available covariates, negative wealth shocks would truly be random. In this case, conditioning on the probability of receiving a negative wealth shock as a function of these covariates – the propensity scores [@rosenbaum] – can be used to remove the effect of confounding, either by regression, matching, or weighting [@imbens_rubin]. For the second issue – confounding by indication – marginal structural models [MSM, @msm] and more recently, penalized spline of propensity methods in treatment comparisons [PENCOMP, @zhou_t], have been used to account for confounding by the time-dependence association of the cognitive measures, either by weighting using the inverse probability of treatment actually received based on the previous values of the time-varying covariates and outcomes (MSM), or by imputation of the missing counterfactual values (PENCOMP). For censoring by death, MSMs have typically been extended by multiplying the treatment assignment weights with the inverse of the predicted probability of death [@weuve]. The issue with this approach – perhaps under appreciated – is that the resulting pseudo-population is not only balanced with respect to exposure “assignment”, but also “immortal”, in the sense that those more likely to die are upweighted so that the population over time resembles that would have been obtained in 