提出时序因果解释树，可追踪多步事件与智能体反馈。
Causal Explanations Over Time: Articulated Reasoning for Interactive Environments
- 用递归解释树建模时间动态因果关系
- 在合成时间序列和2D游戏上验证有效性和可解释性
- 适合需要长期推理的交互式系统研究者
结构化因果解释（SCE）可基于因果模型自动生成自然语言解释，但仅适用于小规模数据。难以应对多时间步的因果演化或涉及智能体反馈循环的行为分析。为此，本文将SCE推广为递归形式的解释树，以捕捉原因之间的时序交互。在合成时间序列数据和2D网格游戏环境中验证了该方法的优势，并与基础SCE及其他现有因果解释方法进行了对比，结果表明其在复杂动态场景中具有更强的表达能力与解释力。
原文摘要 · Abstract (English)
Structural Causal Explanations (SCEs) can be used to automatically generate explanations in natural language to questions about given data that are grounded in a (possibly learned) causal model. Unfortunately they work for small data only. In turn they are not attractive to offer reasons for events, e.g., tracking causal changes over multiple time steps, or a behavioral component that involves feedback loops through actions of an agent. To this end, we generalize SCEs to a (recursive) formulation of explanation trees to capture the temporal interactions between reasons. We show the benefits of this more general SCE algorithm on synthetic time-series data and a 2D grid game, and further compare it to the base SCE and other existing methods for causal explanations.
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