Identifying root causes of anomalies in causal processes is vital across
disciplines. Once identified, one can isolate the root causes and implement
necessary measures to restore the normal operation. Causal processes are often
modelled as graphs with entities being nodes and their paths/interconnections
as edge. Existing work only consider the contribution of nodes in the
generative process, thus can not attribute the outlier score to the edges of
the mechanism if the anomaly occurs in the connections. In this paper, we
consider both individual edge and node of each mechanism when identifying the
root causes. We introduce a noisy functional causal model to account for this
purpose. Then, we employ Bayesian learning and inference methods to infer the
noises of the nodes and edges. We then represent the functional form of a
target outlier leaf as a function of the node and edge noises. Finally, we
propose an efficient gradient-based attribution method to compute the anomaly
attribution scores which scales linearly with the number of nodes and edges.
Experiments on simulated datasets and two real-world scenario datasets show
better anomaly attribution performance of the proposed method compared to the
baselines. Our method scales to larger graphs with more nodes and edges.

通过考虑每个机制的个体边缘和节点，引入了噪声功能因果模型来确定异常的根本原因，并采用贝叶斯学习和推断方法来推测节点和边缘的噪声，最后提出了一种高效的基于梯度的归因方法来计算异常归因分数，此方法在模拟数据集和两个真实场景数据集上表现出更好的异常归因性能。

噪声机制下异常值的根本原因解释

Root Cause Explanation of Outliers under Noisy Mechanisms

Causal processes in nature may contain cycles, and real datasets may violate
causal sufficiency as well as contain selection bias. No constraint-based
causal discovery algorithm can currently handle cycles, latent variables and
selection bias (CLS) simultaneously. I therefore introduce an algorithm called
Cyclic Causal Inference (CCI) that makes sound inferences with a conditional
independence oracle under CLS, provided that we can represent the cyclic causal
process as a non-recursive linear structural equation model with independent
errors. Empirical results show that CCI outperforms CCD in the cyclic case as
well as rivals FCI and RFCI in the acyclic case.

本文介绍了一种称为循环因果推断（CCI）的算法，能够在条件独立神经元操作符下对循环因果过程进行有效推断，如将循环因果过程表示为非递归线性结构方程模型与独立误差。实证结果表明，CCI 在循环情况下优于 CCD，且在无环情况下与 FCI 和 RFCI 竞争力不相上下。