Sharp bounds on the causal effects in randomized experiments with truncation-by-death

Many randomized experiments suffer from the truncation-by-death problem where potential outcomes are not defined for some subpopulations. For example, in medical trials, quality-of-life measures are only defined for surviving patients. In this article, I derive the sharp bounds on causal effects under various assumptions. My identification analysis is based on the idea that the truncation-by-death problem can be formulated as the contaminated data problem. The proposed analytical techniques can be applied to other settings in causal inference including the estimation of direct and indirect effects and the analysis of three-arm randomized experiments with noncompliance. (c) 2007 Elsevier B.V. All rights reserved.