用非递归结构方程模型实现时间因果推理，支持循环依赖与反馈。
Temporal Causal Reasoning with (Non-Recursive) Structural Equation Models
- 将结构方程视为变量动态转化机制，结合反事实推理与时间逻辑
- 提出CPLTL逻辑，可处理互依赖过程和反馈回路，无需递归限制
- 建立新模型等价性概念，支持高效模型检验
结构方程模型（SEM）是表示因果模型中变量间因果依赖的标准方法。本文提出一种新的实际因果推理视角：将SEM视为将外生变量动态转化为内生变量动态的机制。这使得我们能够将反事实因果推理与现有时间逻辑形式化结合，并引入一种名为CPLTL的时间逻辑，用于对这类结构进行因果推理。我们证明，传统上要求模型为“递归”（即依赖图无环）的限制在本方法中并非必需，从而可处理相互依赖的过程与反馈回路。最后，我们提出了时序因果模型的新等价性概念，并证明了CPLTL具有高效的模型检验算法。
原文摘要 · Abstract (English)
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.
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