Testing Identifiability of Causal Effects
  This paper concerns the probabilistic evaluation of the effects of actions in
the presence of unmeasured variables. We show that the identification of causal
effect between a singleton variable X and a set of variables Y can be
accomplished systematically, in time polynomial in the number of variables in
the graph. When the causal effect is identifiable, a closed-form expression can
be obtained for the probability that the action will achieve a specified goal,
or a set of goals.
