提出新方法，更精准判断论证中因果关系
Beyond But-for Test: Counterfactual Explanation in Abstract Argumentation via Actual Causality (Extended Version)
- 用方程建模论证接受条件，支持同时干预多个论点
- 可固定某些论点实际状态，提升分析可靠性
- 适用于预设、过度决定等复杂场景，适合逻辑推理研究者
抽象论证中的反事实解释需回答：若某些论点的状态改变，核心论点是否仍被接受？现有方法仅依赖但因测试，难以处理更精细的反事实条件。本文提出基于干预的反事实推理框架，将论点接受条件编码为方程，并定义干预算子，支持同时改变多组论点状态，且可固定见证论点的实际标签。基于Halpern-Pearl的精确定义，该方法超越但因测试，能正确识别预设与过度决定等结构中的真正原因。实验表明，该方法在表达能力和可靠性上均优于已有方法。
原文摘要 · Abstract (English)
Counterfactual explanation in abstract argumentation calls for an answer to the what-if query: would the topic argument still be accepted if the status of certain other arguments were changed? Existing approaches are limited to the but-for test and fail to accommodate more refined counterfactual conditions. To overcome these limitations, we introduce an intervention-based counterfactual reasoning framework in abstract argumentation. Our approach encodes the acceptance conditions of arguments as equations, then defines an intervention operator that supports (1) changing sets of arguments simultaneously, and (2) fixing witness arguments to their actual labels. Guided by the refined counterfactual condition introduced in the Halpern-Pearl definition, our method goes beyond the but-for test, thereby correctly identifying causes in argumentation structures such as Preemption and Overdetermination. Through comparison, we show that our method surpasses prior methods in both expressiveness and reliability.
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