 A closely related counterfactual quantity is what Pearl (2009) calls the probability of sufficiency. Suppose that Teresa, unlike Ted, was not exposed to asbestos, and did not develop lung cancer. The probability of sufficiency is the probability that she would have suffered lung cancer if she had been exposed. That is, the probability of sufficiency is the probability that if the cause were added to a situation in which it and the effect was absent, it would have resulted in the effect occurring. The probability of sufficiency is closely related to the quantity that Sheps (1958) called the relative difference, and that Cheng (1997) calls the causal power. Cheng’s terminology reflects the idea that the probability of sufficiency of C for E is the power of C to bring about E in cases where E is absent. As in the case of the probability of necessity, if one does not have a complete structural equation model, but only a Causal Bayes Net or Semi-Markov Causal Model, it is usually only possible to put upper and lower bounds on the probability of sufficiency. Using the probabilities from the previous example, the probability of sufficiency of asbestos for lung cancer would be between .05 (5/94) and .12 (11/94).