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.