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Saturday, August 27, 2022

Counterfactuals and the logic of causal selection

Quillien, T., & Lucas, C. G. (2022, June 13)


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.

From the General Discussion

Judgments of causation are closely related to assignments of blame, praise, and moral responsibility.  For instance, when two cars crash at an intersection, we say that the accident was caused by the driver who went through a red light (not by the driver who went through a green light; Knobe and Fraser, 2008; Icard et al., 2017; Hitchcock and Knobe, 2009; Roxborough and Cumby, 2009; Alicke, 1992; Willemsen and Kirfel, 2019); and we also blame that driver for the accident. According to some theorists, the fact that we judge the norm-violator to be blameworthy or morally responsible explains why we judge that he was the cause of the accident. This might be because our motivation to blame distorts our causal judgment (Alicke et al., 2011), because our intuitive concept of causation is inherently normative (Sytsma, 2021), or because of pragmatics confounds in the experimental tasks that probe the effect of moral violations on causal judgment (Samland & Waldmann, 2016).

Under these accounts, the explanation for why moral considerations affect causal judgment should be completely different than the explanation for why other factors (e.g.,prior probabilities, what happened in the actual world, the causal structure of the situation) affect causal judgment. We favor a more parsimonious account: the counterfactual approach to causal judgment (of which our theory is one instantiation) provides a unifying explanation for the influence of both moral and non-moral considerations on causal judgment (Hitchcock & Knobe, 2009)16.

Finally, many formal theories of causal reasoning aim to model how people make causal inferences (e.g. Cheng, 1997; Griffiths & Tenenbaum, 2005; Lucas & Griffiths, 2010; Bramley et al., 2017; Jenkins & Ward, 1965). These theories are not concerned with the problem of causal selection, the focus of the present paper. It is in principle possible that people use the same algorithms they use for causal inference when they engage in causal selection, but in practice models of causal inference have not been able to predict how people select causes (see Quillien and Barlev, 2022; Morris et al., 2019).