Most counterfactual inference frameworks traditionally assume acyclic structural causal models (SCMs), i.e. directed acyclic graphs (DAGs). However, many real-world systems (e.g. biological systems) contain feedback loops or cyclic dependencies that violate acyclicity. In this work, we study counterfactual inference in cyclic SCMs under shift-scale interventions, i.e., soft, policy-style changes that rescale and/or shift a variable's mechanism.

翻译：传统反事实推理框架通常假设结构因果模型（SCMs）具有无环性，即采用有向无环图（DAGs）表示。然而，许多现实世界系统（例如生物系统）包含反馈回路或循环依赖关系，这违背了无环性假设。本研究探讨在移位-尺度干预下循环结构因果模型中的反事实推理问题，此类干预属于柔性、策略式变更，通过重新缩放和/或偏移变量的生成机制实现。