Temporal Causal Reasoning with (Non-Recursive) Structural Equation Models
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arXiv
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| Format: | Preprint |
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2025
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| author | Gladyshev, Maksim Alechina, Natasha Dastani, Mehdi Doder, Dragan Logan, Brian |
| author_facet | Gladyshev, Maksim Alechina, Natasha Dastani, Mehdi Doder, Dragan Logan, Brian |
| contents | Structural Equation Models (SEM) are the standard approach to representing causal dependencies between variables in causal models. In this paper we propose a new interpretation of SEMs when reasoning about Actual Causality, in which SEMs are viewed as mechanisms transforming the dynamics of exogenous variables into the dynamics of endogenous variables. This allows us to combine counterfactual causal reasoning with existing temporal logic formalisms, and to introduce a temporal logic, CPLTL, for causal reasoning about such structures. We show that the standard restriction to so-called \textit{recursive} models (with no cycles in the dependency graph) is not necessary in our approach, allowing us to reason about mutually dependent processes and feedback loops. Finally, we introduce new notions of model equivalence for temporal causal models, and show that CPLTL has an efficient model-checking procedure. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_10190 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Temporal Causal Reasoning with (Non-Recursive) Structural Equation Models Gladyshev, Maksim Alechina, Natasha Dastani, Mehdi Doder, Dragan Logan, Brian Artificial Intelligence Logic in Computer Science Structural Equation Models (SEM) are the standard approach to representing causal dependencies between variables in causal models. In this paper we propose a new interpretation of SEMs when reasoning about Actual Causality, in which SEMs are viewed as mechanisms transforming the dynamics of exogenous variables into the dynamics of endogenous variables. This allows us to combine counterfactual causal reasoning with existing temporal logic formalisms, and to introduce a temporal logic, CPLTL, for causal reasoning about such structures. We show that the standard restriction to so-called \textit{recursive} models (with no cycles in the dependency graph) is not necessary in our approach, allowing us to reason about mutually dependent processes and feedback loops. Finally, we introduce new notions of model equivalence for temporal causal models, and show that CPLTL has an efficient model-checking procedure. |
| title | Temporal Causal Reasoning with (Non-Recursive) Structural Equation Models |
| topic | Artificial Intelligence Logic in Computer Science |
| url | https://arxiv.org/abs/2501.10190 |