The goal of Root Cause Analysis (RCA) is to explain why an anomaly occurred by identifying where the fault originated. Several recent works model the anomalous event as resulting from a change in the causal mechanism at the root cause, i.e., as a soft intervention. RCA is then the task of identifying which causal mechanism changed. In real-world applications, one often has either few or only a single sample from the post-intervention distribution: a severe limitation for most methods, which assume one knows or can estimate the distribution. However, even those that do not are statistically ill-posed due to the need to probe regression models in regions of low probability density. In this paper, we propose simple, efficient methods to overcome both difficulties in the case where there is a single root cause and the causal graph is a polytree. When one knows the causal graph, we give guarantees for a traversal algorithm that requires only marginal anomaly scores and does not depend on specifying an arbitrary anomaly score cut-off. When one does not know the causal graph, we show that the heuristic of identifying root causes as the variables with the highest marginal anomaly scores is causally justified. To this end, we prove that anomalies with small scores are unlikely to cause those with larger scores in polytrees and give upper bounds for the likelihood of causal pathways with non-monotonic anomaly scores.

翻译：根本原因分析（RCA）的目标是通过识别故障起源来解释异常发生的原因。近年来的一些研究将异常事件建模为根节点处因果机制变化的结果，即软干预。RCA的任务便是识别哪个因果机制发生了变化。在实际应用中，通常只能从干预后分布中获得少量甚至单个样本：这对大多数方法构成了严重限制，因为这些方法假设已知或能够估计分布。然而，即使那些不依赖分布估计的方法，由于需要在低概率密度区域探测回归模型，在统计上仍是不适定的。本文针对单根节点且因果图为多树结构的情况，提出了简单高效的方法来克服这两类困难。当已知因果图时，我们为一种遍历算法提供理论保证，该算法仅需边际异常分数，且不依赖于设定任意异常分数阈值。当因果图未知时，我们证明了将具有最高边际异常分数的变量识别为根节点的启发式方法具有因果合理性。为此，我们证明了在多树结构中，低异常分数变量引发高异常分数异常的可能性较低，并给出了非单调异常分数因果路径可能性的上界。