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

When the prediction of a black-box machine learning model deviates from the
true observation, what can be said about the reason behind that deviation? This
is a fundamental and ubiquitous question that the end user in a business or
industrial AI application often asks. The deviation may be due to a sub-optimal
black-box model, or it may be simply because the sample in question is an
outlier. In either case, one would ideally wish to obtain some form of
attribution score -- a value indicative of the extent to which an input
variable is responsible for the anomaly.
In the present paper we address this task of ``anomaly attribution,''
particularly in the setting in which the model is black-box and the training
data are not available. Specifically, we propose a novel likelihood-based
attribution framework we call the ``likelihood compensation (LC),'' in which
the responsibility score is equated with the correction on each input variable
needed to attain the highest possible likelihood. We begin by showing formally
why mainstream model-agnostic explanation methods, such as the local linear
surrogate modeling and Shapley values, are not designed to explain anomalies.
In particular, we show that they are ``deviation-agnostic,'' namely, that their
explanations are blind to the fact that there is a deviation in the model
prediction for the sample of interest. We do this by positioning these existing
methods under the unified umbrella of a function family we call the
``integrated gradient family.'' We validate the effectiveness of the proposed
LC approach using publicly available data sets. We also conduct a case study
with a real-world building energy prediction task and confirm its usefulness in
practice based on expert feedback.

介绍了一种基于可能性补偿的异常归属框架，用于黑匣子机器学习模型下对异常进行归属。通过对比现有的模型无关解释方法的局限性，利用独立梯度家族函数来辨别异常，证明了该方法的实用性和有效性。