Counterfactuals and the logic of causal selection

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

https://doi.org/10.31234/osf.io/ts76y

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.

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).

The research is here.

Inference from explanation.

Kirfel, L., Icard, T., & Gerstenberg, T.
(2020, May 22).
https://doi.org/10.31234/osf.io/x5mqc

Abstract

What do we learn from a causal explanation? Upon being told that "The fire occurred because a lit match was dropped", we learn that both of these events occurred, and that there is a causal relationship between them. However, causal explanations of the kind "E because C" typically disclose much more than what is explicitly stated. Here, we offer a communication-theoretic account of causal explanations and show specifically that explanations can provide information about the extent to which a cited cause is normal or abnormal, and about the causal structure of the situation. In Experiment 1, we demonstrate that people infer the normality of a cause from an explanation when they know the underlying causal structure. In Experiment 2, we show that people infer the causal structure from an explanation if they know the normality of the cited cause. We find these patterns both for scenarios that manipulate the statistical and prescriptive normality of events. Finally, we consider how the communicative function of explanations, as highlighted in this series of experiments, may help to elucidate the distinctive roles that normality and causal structure play in causal explanation.

Conclusion

In this paper, we investigate the communicative dimensions of explanation, revealing some of the rich and subtle inferences people draw from them. We find that people are able to infer additional information from a causal explanation beyond what was explicitly communicated, such as causal structure and normality of the causes.  Our studies show that people make these inferences in part by appeal to what they themselves would judge reasonable to say across different possible scenarios. The overall pattern of judgments and inferences brings us closer to a full understanding of how causal explanations function inhuman discourse and behavior, while also raising new questions concerning the prominent role of norms in causal judgment and the function of causal explanation more broadly.

Editor's Note: This research has significant implications for psychotherapy.

The Psychology of Moral Reasoning

Moral Reasoning

This article is found in the public domain here.