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Computer scientists and philosophers have proposed Bayes nets as formal representations of causal systems, and many psychologists have claimed them as plausible mental representations. One purported advantage of Bayes nets is that they provide a theory of counterfactual conditionals, such as If Calvin had been at the party, Miriam would have left early. This talk compares two proposed Bayes-net theories as models of people's understanding of counterfactuals. The results from several experiments show that neither theory does a perfect job of predicting people's judgments, but one theory clearly outperforms the other. We consider whether it's best to modify Bayes nets or to abandon them within this domain.