Counterfactuals and the logic of causal selection

Abstract

Everything that happens has a multitude of causes, but people make causal judgments effortlessly. How do people select one particular cause (e.g. the lightning bolt that set the forest ablaze) out of the set of factors that contributed to the event (the oxygen in the air, the dry weather. . . )? Cognitive scientists have suggested that people make causal judgments about an event by simulating alternative ways things could have happened. We argue that this counterfactual theory explains many features of human causal intuitions, given two simple assumptions. First, people tend to imagine counterfactual possibilities that are both a priori likely and similar to what actually happened. Second, people judge that a factor C caused effect E if C and E are highly correlated across these counterfactual possibilities. In a reanalysis of existing empirical data, and a set of new experiments, we find that this theory uniquely accounts for people’s causal intuitions.

Calculated Comparisons: Manufacturing Societal Causal Judgments by Implying Different Counterfactual Outcomes

“…The current research is informed by counterfactual theories of causal judgment (Gerstenberg, Goodman, Lagnado, & Tenenbaum, 2021; Lewis, 1973; Lucas & Kemp, 2015; Mackie, 1974; Quillien & Lucas, 2023; Woodward, 2005). According to this view, people determine causality by comparing the actual, known outcome to a counterfactual that informs what would have happened if the candidate cause had been absent.…”

Making a positive difference: Criticality in groups

“…However, when both player C and D succeeded, then the team would have won even if player C (or D) had failed. Lagnado et al (2013) found that both criticality and pivotality were important for capturing participants' responsibility judgments (see also Quillien & Lucas, 2022).…”

Making a positive difference: Criticality in groups

“…Within this framework, ex-post responsibility is constructed as a function of both ex-ante criticality and ex-post pivotality. A combination of criticality and pivotality explains responsibility attributions better than each component separately (see also Quillien & Lucas, 2022). To test the criticality-pivotality framework, we used several formalizations of criticality, which we termed the anticipated pivotality model, the heuristic model and the necessity model (Lagnado, Gerstenberg, & Zultan, 2013, p. 1054-1056.…”