  In causal inference, and specifically in the \textit{Causes of Effects}
problem, one is interested in how to use statistical evidence to understand
causation in an individual case, and so how to assess the so-called {\em
probability of causation} (PC).
  The answer relies on the potential responses, which can incorporate
information about what would have happened to the outcome as we had observed a
different value of the exposure. However, even given the best possible
statistical evidence for the association between exposure and outcome, we can
typically only provide bounds for the PC. Dawid et al. (2016) highlighted some
fundamental conditions, namely, exogeneity, comparability, and sufficiency,
required to obtain such bounds, based on experimental data. The aim of the
present paper is to provide methods to find, in specific cases, the best
subsample of the reference dataset to satisfy such requirements. To this end,
we introduce a new variable, expressing the desire to be exposed or not, and we
set the question up as a model selection problem. The best model will be
selected using the marginal probability of the responses and a suitable prior
proposal over the model space. An application in the educational field is
presented.
