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29 - Who Caused It? Different Effects of Statistical and Prescriptive Abnormality on Causal Selection in Chains

from Part III - Applications

Published online by Cambridge University Press:  17 May 2025

By
Neele Engelmann  and
Lara Kirfel
Edited by
Kevin Tobia
Kevin Tobia
Affiliation:
Georgetown University, Washington DC
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Summary

Determining proximate causation is crucial for decisions about legal liability, but how judges select proximate causes is a notoriously disputed issue. Knobe and Shapiro (2020) recently argued that the perceived (ab)normality of causal factors explains both laypeople’s and legal experts’ causal selection patterns. While a large body of psychological research shows that people indeed often select abnormal factors as most important, this research has focused on a very narrow set of scenarios: two simultaneously occurring but independent causes that either conjunctively or disjunctively bring about some outcome. We here explore whether normality also guides causal selection in structures that may be more typical of many legal scenarios: successively occurring causes that are themselves causally connected (causal chains). Comparing effects of both statistical and prescriptive abnormality on causal selection in chains, we only find a tendency to select abnormal causes for manipulations of prescriptive but not statistical normality. Moreover, judgments about the counterfactual relevance of causes or about their suitability as targets of intervention were only moderately correlated with causal selection patterns. The interplay between causal structure and different kinds of (ab)normality in people’s reasoning about proximate causation may thus be more complex than is currently recognized.

Keywords

abnormalitycausationcausal chainsnormalitynorms
Type
Chapter
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The Cambridge Handbook of Experimental Jurisprudence , pp. 481 - 501
Publisher: Cambridge University Press
Print publication year: 2025

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