Abstract

Many studies aim to estimate treatment effects on outcomes that are defined only for individuals who experience a post-treatment event. For example, the effect of cancer therapies on quality of life is only well defined among individuals who are alive. Similarly, the effect of vaccines on post-infection outcomes is only of interest among individuals who become infected. A naive comparison of outcomes conditional on such post-treatment events generally lacks a causal interpretation, even when treatment is randomly assigned.   In this talk, I discuss causal contrasts for outcomes conditional on post-treatment events, including principal stratum effects and conditional separable effects. I then derive identification results for these estimands and discuss their interpretation and the conditions under which they might, in principle, be falsified. I illustrate the relevance of these results for clinical trials of cancer therapies and vaccines, and I conclude by revisiting the classical birth weight paradox in epidemiology.