Explanations of Counterfactual Inferences

Explanations of Counterfactual Inferences Brian J. Edwards (Brian.Edwards@u.northwestern.edu) Department of Psychology, Northwestern University, 2029 Sheridan Rd., Evanston, IL 60208 USA Lance J. Rips (Rips@northwestern.edu) Department of Psychology, Northwestern University, 2029 Sheridan Rd., Evanston, IL 60208 USA Abstract the actual state to the counterfactual state and then tracing the consequences of that intervention (Pearl, 2000; see also Woodward, 2003). The intervention severs the causal link between the antecedent and its immediate causes, and as a result of this “graph surgery,” the counterfactual states of upstream events would be the same as in the actual world. However, downstream events that are a consequence of the antecedent would change states according to the causal laws governing the system. To illustrate this approach, consider a causal chain A → B → C and a counterfactual antecedent If B had not occurred… (in the actual world, A, B, and C all occurred). A person using pruning theory would intervene on B to change the state of B from present to absent. Since upstream events (A) are unaffected by this intervention, A would still have been present in the counterfactual world. But since C is an effect of B, B’s absence would in turn cause C to be absent. Pruning theory might appeal to reasoners in two ways. First, by keeping all the events that are causally prior to the antecedent in the same states as in the actual world, pruning theory creates a counterfactual world that is maximally similar to the actual world with respect to these prior events. Second, the pruning approach makes counterfactual thinking computationally easy. The strategy of always keeping prior events in their original states allows reasoners to avoid the cognitively challenging process of reasoning backwards to determine the counterfactual states of upstream causes. However, other researchers have questioned whether the type of change pruning theory proposes is necessarily the most reasonable way to modify the causal system in the counterfactual situation (e.g., Hiddleston, 2005). One criticism of pruning theory is that it is very disruptive to the structure of a causal system and can require reasoners to violate causal laws. Consider a deterministic causal system in which A, without exception, always causes B. In this setting, one might be reluctant to imagine a counterfactual world in which A occurred, but B did not occur (e.g., in answering the question If B had not occurred, would A have occurred?). Thus, when reasoning about this counterfactual scenario, one might be more likely to infer that the reason B did not occur was that A did not occur, and the absence of A caused B to be absent too (Hiddleston, 2005). We will call this alternative minimal-network theory. When the causal links are probabilistic (i.e., A sometimes causes B), however, minimal-network theory proposes that A might or might not have occurred, since either possibility is “legal” in accordance with the system’s causal laws. When engaging in counterfactual thought, people must imagine changes to the actual state of the world. In this study, we investigated how people reason about counterfactual scenarios by asking participants to make counterfactual inferences about a series of causal devices (i.e., answer questions such as If component X had not operated [had failed], would components Y, Z, and W have operated?) and to explain their reasoning. Participants avoided breaking deterministic causal links (i.e., W always causes X), but were willing to break probabilistic causal links (i.e., W sometimes causes X) to keep prior causal events in the same states as in the actual world. Participants’ explanations supported this pattern of inferences. When the causal links were deterministic, participants reasoned diagnostically to infer that the states of prior causal events would have been different in the counterfactual world. In contrast, when the links were probabilistic, participants cited the links’ unreliability as an explanation for why the states of prior causal events would have been the same as in the actual world. Additionally, participants who were told that a component “had failed” (vs. “had not operated”) were more likely to attribute the state of that component to it being “internally broken” and infer that causally upstream components would have operated. Our results suggest that people use their explanation of the antecedent event (the “if” clause) to guide their counterfactual inferences. We discuss the implications of these findings for two rival Bayes-net theories of counterfactual reasoning: Pearl’s (2000) and Hiddleston’s (2005). Keywords: Counterfactuals, causation, explanation Introduction People often engage in counterfactual reasoning (e.g., If I hadn’t partied the night before the exam, then I would have passed the exam) to second-guess decisions, attribute credit or blame, and diagnose causal relations (see Byrne, 2005, for a review). Reasoning about counterfactual scenarios such as the preceding example requires imagining changes to the actual state of the world—for instance, imagining a counterfactual world in which I hadn’t partied the night before the exam. One of the central issues in the study of counterfactual reasoning is how people re-imagine the world to satisfy the antecedent of a counterfactual scenario. (The antecedent is the “if” clause, and we will refer to the “then” clause as the consequent.) In particular, what types of events do people keep the same in the actual and counterfactual worlds and what types of events do people change? One way people might reason about counterfactual scenarios, which we will call pruning theory, is by using an intervention to change the state of the antecedent event from