  I develop a model of a randomized experiment with a binary intervention and a
binary outcome. Potential outcomes in the intervention and control groups give
rise to four types of participants. Fixing ideas such that the outcome is
mortality, some participants would live regardless, others would be saved,
others would be killed, and others would die regardless. These potential
outcome types are not observable. However, I use the model to develop
estimators of the number of participants of each type. The model relies on the
randomization within the experiment and on deductive reasoning. I apply the
model to an important clinical trial, the PROWESS trial, and I perform a Monte
Carlo simulation calibrated to estimates from the trial. The reduced form from
the trial shows a reduction in mortality, which provided a rationale for FDA
approval. However, I find that the intervention killed two participants for
every three it saved.
