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<p>This paper develops a formal methodology for capturing and representing the semantics of causal expressions in natural languages. Focusing on two causative constructions—covert causatives (change-of-state verbs) and overt causatives (the verb <i>cause</i>)—it provides a proof of concept for analyzing the distinguished meanings of different causative constructions. We adopt the formal framework of Structural Equation Modeling (SEM) to analyze causality and integrate it into model-theoretic semantics for interpreting causal statements. In our approach, the selection of a cause within a particular construction depends on its inclusion in <i>a sufficient set of conditions</i> that bring about the effect, as well as on specific properties of the cause itself. To formalize this process, we introduce the concept of causative-construction selection (CC-selection), which captures how speakers select a causative construction that aligns with the relational structure between states of affairs. For each relevant condition within the sufficient set, CC-selection determines whether it can be encoded as the cause in a statement articulated through a specific causative construction, thereby describing a particular state of affairs. We argue that CC-selection plays a central role in shaping the meaning of causal statements. By leveraging the SEM framework, CC-selection effects can be formally explained through contrasts within the structure of a model. For instance, notions of <i>sufficiency</i> and <i>necessity</i>, which play a crucial role in these selections, are rigorously defined within SEM, allowing for a precise account of CC-selection effects. This paper further illustrates how CC-selection accounts for contrastive inference patterns across constructions. By focusing on the two causative constructions central to our discussion, it resolves longstanding puzzles associated with change-of-state (CoS) verbs. The proposed framework establishes a foundation for the systematic study of causal language, bridging semantics and philosophy while providing tools to investigate the interplay between causative constructions and their associated causal meanings.</p>

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Modeling linguistic causation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月1日

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  • Rebekah Baglini,
  • Elitzur A. Bar-Asher Siegal

摘要

This paper develops a formal methodology for capturing and representing the semantics of causal expressions in natural languages. Focusing on two causative constructions—covert causatives (change-of-state verbs) and overt causatives (the verb cause)—it provides a proof of concept for analyzing the distinguished meanings of different causative constructions. We adopt the formal framework of Structural Equation Modeling (SEM) to analyze causality and integrate it into model-theoretic semantics for interpreting causal statements. In our approach, the selection of a cause within a particular construction depends on its inclusion in a sufficient set of conditions that bring about the effect, as well as on specific properties of the cause itself. To formalize this process, we introduce the concept of causative-construction selection (CC-selection), which captures how speakers select a causative construction that aligns with the relational structure between states of affairs. For each relevant condition within the sufficient set, CC-selection determines whether it can be encoded as the cause in a statement articulated through a specific causative construction, thereby describing a particular state of affairs. We argue that CC-selection plays a central role in shaping the meaning of causal statements. By leveraging the SEM framework, CC-selection effects can be formally explained through contrasts within the structure of a model. For instance, notions of sufficiency and necessity, which play a crucial role in these selections, are rigorously defined within SEM, allowing for a precise account of CC-selection effects. This paper further illustrates how CC-selection accounts for contrastive inference patterns across constructions. By focusing on the two causative constructions central to our discussion, it resolves longstanding puzzles associated with change-of-state (CoS) verbs. The proposed framework establishes a foundation for the systematic study of causal language, bridging semantics and philosophy while providing tools to investigate the interplay between causative constructions and their associated causal meanings.