Probabilistic Evaluation of Sequential Plans from Causal Models with
  Hidden Variables
  The paper concerns the probabilistic evaluation of plans in the presence of
unmeasured variables, each plan consisting of several concurrent or sequential
actions. We establish a graphical criterion for recognizing when the effects of
a given plan can be predicted from passive observations on measured variables
only. When the criterion is satisfied, a closed-form expression is provided for
the probability that the plan will achieve a specified goal.
