Preemption in Singular Causation Judgments - A Computational Model (Proceedings version; Higher Level Cognition Award of the Cognitive Science Society)

Preemption in Singular Causation Judgments - A Computational Model (Proceedings version; Higher Level Cognition Award of the Cognitive Science Society)

Abstract

Causal queries about singular cases are ubiquitous, yet the question of how we assess whether a particular outcome was actually caused by a specific potential cause turns out to be difficult to answer. Relying on the causal power approach, Cheng and Novick (2005) proposed a model of causal attribution intended to help answering this question. We challenge this model, both conceptually and empirically. The central problem of this model is that it treats the presence of sufficient causes as necessarily causal in singular causation, and thus neglects that causes can be preempted in their efficacy. Also, the model does not take into account that reasoners incorporate uncertainty about the underlying causal structure and strength of causes when making causal inferences. We propose a new measure of causal attribution and embed it into our structure induction model of singular causation (SISC). Two experiments support the model.

Publication
In G. Gunzelmann, A. Howes, T. Tenbrink, & E. Davelaar (Eds.), Proceedings of the 39th Annual Conference of the Cognitive Science Society (pp. 1126-1131). Austin, TX - Cognitive Science Society.
Higher Level Cognition Award of the Cognitive Science Society: https://cognitivesciencesociety.org/conference-awards/
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Simon Stephan
Research Scientist in the field of Cognitive Science at the

My research interest is computational cognitive science. I’m particularly active in the field of causal learning and causal reasoning.