We consider the proposition that reasoners represent causal conditionals such as "if John studies hard, he will do well in the test" as a causal model in which the antecedent ("John studies hard") is a potential cause of the consequent ("John does well in the test"). Some studies suggest that reasoners ignore alternative causes of the consequent in predictive judgments. Similarly, reasoners may not fully consider alternative causes in diagnostic judgments either. We tested these assumptions in a comparison of 2 causal models with and without alternative causes. In Experiments 1 and 2, only the model with alternative causes tended to overestimate predictive and diagnostic judgments. In Experiment 3, we tested whether the causal models account for the participants' judgments of the probability of the conditional. However, neither model's predictions were accurate. Based on the assumption that probability judgments only follow ordinal relations, we tested qualitative, rather than quantitative predictions of the causal models in Experiments 4 and 5. Participants provided predictive and diagnostic judgments for causal scenarios they observed in the experiments. The results suggest that reasoners consider alternative causes. Finally, in Experiment 6, participants considered pairs of causal conditionals, matched in causal power but differing in the probability of alternative causes. On average, participants preferred to bet on the predictive conclusions of those conditionals that had a higher probability of alternative causes. Because of the uncertain metric properties of probability judgments, we conclude that reasoners likely consider alternative causes in predictive and diagnostic judgments.