从干预数据中识别链式反应系统的因果结构，仅需少量干预即可准确恢复。
Causal Discovery in Action: Learning Chain-Reaction Mechanisms from Interventions
- 通过阻断干预阻止组件激活，实现因果结构唯一可识别。
- 有限样本下误差呈指数下降，样本复杂度为对数级。
- 适用于延迟或重叠效应场景，优于传统观测方法。
因果发现通常在一般动态系统中极具挑战性，因缺乏强结构假设时，即使有干预数据也无法唯一识别潜在因果图。然而，许多真实系统具有方向性、级联式的结构：组件按顺序激活，上游失效会抑制下游效应。本文研究此类链式反应系统中的因果发现，证明仅通过阻断干预（阻止单个组件激活）即可唯一识别因果结构。提出一种最小估计器，具备有限样本保证，实现误差指数衰减与对数级样本复杂度。在合成模型及多样链式反应环境上的实验表明，仅需少量干预即可可靠恢复因果关系，而基于观测的启发式方法在存在延迟或重叠因果效应的场景中失效。
原文摘要 · Abstract (English)
Causal discovery is challenging in general dynamical systems because, without strong structural assumptions, the underlying causal graph may not be identifiable even from interventional data. However, many real-world systems exhibit directional, cascade-like structure, in which components activate sequentially and upstream failures suppress downstream effects. We study causal discovery in such chain-reaction systems and show that the causal structure is uniquely identifiable from blocking interventions that prevent individual components from activating. We propose a minimal estimator with finite-sample guarantees, achieving exponential error decay and logarithmic sample complexity. Experiments on synthetic models and diverse chain-reaction environments demonstrate reliable recovery from a few interventions, while observational heuristics fail in regimes with delayed or overlapping causal effects.
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