Assessing the likelihood that a counterfactual event would have happened involves contrasting a factual outcome withthe counterfactual alternative. In many situations, the number of alternatives will influence the perceived closeness of aparticular alternative. For example, losers of a game in which participants guess which door conceals a prize will likelybelieve they were closer to winning when there were three doors compared to six. This reflects accurate probabilisticreasoning because more doors will be associated with a lower probability of winning. However, we test whether thenumber of alternatives has a unique influence on beliefs about counterfactual closeness. Experiments 1 and 2 show that,even when probability is held fixed, people believe counterfactual closeness decreases when there are more alternatives.

Analogical and causal reasoning theories both seek to explainpatterns of inductive inference. Researchers have claimed thatreasoning scenarios incorporating aspects of both analogicalcomparison and causal thinking necessitate a new model of in-ductive inference (Holyoak, Lee, & Lu, 2010; Lee & Holyoak,2008). This paper takes an opposing position, arguing that fea-tures of analogical models make correct claims about infer-ence patterns found among causal analogies, including analo-gies with both generative and preventative relations. Experi-ment 1 demonstrates that analogical inferences for these kindsof causal systems can be explained by alignment of relationalstructure, including higher-order relations. Experiment 2 fur-ther demonstrates that inferences strengthened by matchinghigher-order relations are not guided by the transfer of prob-abilistic information about a cause from base to target. Weconclude that causal analogies behave like analogies in gen-eral—analogical mapping provides candidate inferences whichcan then be reasoned about in the target.

People rely on mechanism knowledge when making causal inferences that involve multiple causal variables. In particular,mechanism knowledge can influence whether people use linear or alternative integration rules to predict how multiplecauses will interact to produce an effect. We examine whether general beliefs about mechanism types whether two causesoperate by the same or different mechanisms might mediate such inferences. Experiment 1 demonstrates that when acausal interaction yields non-linear positive effects, people are more likely to infer that the two causes work via differentmechanisms. Experiment 2 investigates the converse of this inference, showing that people also predict non-linear positiveinteractions more often when they know that two causes have different mechanisms.