A Semantics for Causing, Enabling, and Preventing Verbs Using Structural Causal Models

Abstract

When choosing how to describe what happened, we have a number of causal verbs at our disposal. In this paper, we develop a model-theoretic formal semantics for nine causal verbs that span the categories of CAUSE, ENABLE, and PREVENT. We use structural causal models (SCMs) to represent participants’ mental construction of a scene when assessing the correctness of causal expressions relative to a presented context. Furthermore, SCMs enable us to model events relating both the physical world as well as agents’ mental states. In experimental evaluations, we find that the proposed semantics exhibits a closer alignment with human evaluations in comparison to prior accounts of the verb families.

Publication
Cao A.*, Geiger A.*, Kreiss E.*, Icard T., Gerstenberg T. (2023). A Semantics for Causing, Enabling, and Preventing Verbs Using Structural Causal Models. In Proceedings of the 45th Annual Conference of the Cognitive Science Society.
Date

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