  We develop a Bayesian nonparametric (BNP) approach to evaluate the causal
effect of treatment in a randomized trial where a nonterminal event may be
censored by a terminal event, but not vice versa (i.e., semi-competing risks).
Based on the idea of principal stratification, we define a novel estimand for
the causal effect of treatment on the nonterminal event. We introduce
identification assumptions, indexed by a sensitivity parameter, and show how to
draw inference using our BNP approach. We conduct simulation studies and
illustrate our methodology using data from a brain cancer trial.
