Recent work conceptualized root cause analysis (RCA) of anomalies via
quantitative contribution analysis using causal counterfactuals in structural
causal models (SCMs). The framework comes with three practical challenges: (1)
it requires the causal directed acyclic graph (DAG), together with an SCM, (2)
it is statistically ill-posed since it probes regression models in regions of
low probability density, (3) it relies on Shapley values which are
computationally expensive to find.
In this paper, we propose simplified, efficient methods of root cause
analysis when the task is to identify a unique root cause instead of
quantitative contribution analysis. Our proposed methods run in linear order of
SCM nodes and they require only the causal DAG without counterfactuals.
Furthermore, for those use cases where the causal DAG is unknown, we justify
the heuristic of identifying root causes as the variables with the highest
anomaly score.

通过使用结构因果模型中的因果反事实的定量贡献分析，最近的工作对异常情况的根本原因分析进行了概念化。本文提出了简化、高效的根本原因分析方法，用于识别唯一的根本原因而非定量贡献分析的任务。对于未知因果有向无环图的应用场景，我们将异常得分最高的变量作为根本原因进行启发式验证。

缺失结构知识的异常值根本原因分析

Root Cause Analysis of Outliers with Missing Structural Knowledge

Learning a causal directed acyclic graph from data is a challenging task that
involves solving a combinatorial problem for which the solution is not always
identifiable. A new line of work reformulates this problem as a continuous
constrained optimization one, which is solved via the augmented Lagrangian
method. However, most methods based on this idea do not make use of
interventional data, which can significantly alleviate identifiability issues.
This work constitutes a new step in this direction by proposing a
theoretically-grounded method based on neural networks that can leverage
interventional data. We illustrate the flexibility of the
continuous-constrained framework by taking advantage of expressive neural
architectures such as normalizing flows. We show that our approach compares
favorably to the state of the art in a variety of settings, including perfect
and imperfect interventions for which the targeted nodes may even be unknown.

本文提出了一种新的方法，基于神经网络和流形变换，使用干预数据来学习因果有向无环图，该方法在多种情况下表现出与现有技术相媲美的灵活性和可行性。