  We develop a novel approach to partially identify causal estimands, such as
the average treatment effect (ATE), from observational data. To better satisfy
the stable unit treatment value assumption (SUTVA) we utilize stochastic
counterfactuals within a propensity-prognosis model of the data generating
process. For more precise identification we utilize knowledge of discordant
twin outcomes as evidence for randomness in the data generating process. Our
approach culminates with a constrained optimization problem; the solution gives
upper and lower bounds for the ATE. We demonstrate the applicability of our
introduced methodology with three example applications.
