  For the purpose of causal inference we employ a stochastic model of the data
generating process, utilizing individual propensity probabilities for the
treatment, and also individual and counterfactual prognosis probabilities for
the outcome. We assume a generalized version of the stable unit treatment value
assumption, but we do not assume any version of strongly ignorable treatment
assignment. Instead of conducting a sensitivity analysis, we utilize the
principle of maximum entropy to estimate the distribution of causal effects. We
develop a principled middle-way between extreme explanations of the observed
data: we do not conclude that an observed association is wholly spurious, and
we do not conclude that it is wholly causal. Rather, our conclusions are
tempered and we conclude that the association is part spurious and part causal.
In an example application we apply our methodology to analyze an observed
association between marijuana use and hard drug use.
