  Suppose X and Y are binary exposure and outcome variables, and we have full
knowledge of the distribution of Y, given application of X. From this we know
the average causal effect of X on Y. We are now interested in assessing, for a
case that was exposed and exhibited a positive outcome, whether it was the
exposure that caused the outcome. The relevant "probability of causation", PC,
typically is not identified by the distribution of Y given X, but bounds can be
placed on it, and these bounds can be improved if we have further information
about the causal process. Here we consider cases where we know the
probabilistic structure for a sequence of complete mediators between X and Y.
We derive a general formula for calculating bounds on PC for any pattern of
data on the mediators (including the case with no data). We show that the
largest and smallest upper and lower bounds that can result from any complete
mediation process can be obtained in processes with at most two steps. We also
consider homogeneous processes with many mediators. PC can sometimes be
identified as 0 with negative data, but it cannot be identified at 1 even with
positive data on an infinite set of mediators. The results have implications
for learning about causation from knowledge of general processes and of data on
cases.
