Identifying the underlying reason for a failing dynamic process or otherwise
anomalous observation is a fundamental challenge, yet has numerous industrial
applications. Identifying the failure-causing sub-system using causal
inference, one can ask the question: "Would the observed failure also occur, if
we had replaced the behaviour of a sub-system at a certain point in time with
its normal behaviour?" To this end, a formal description of behaviour of the
full system is needed in which such counterfactual questions can be answered.
However, existing causal methods for root cause identification are typically
limited to static settings and focusing on additive external influences causing
failures rather than structural influences. In this paper, we address these
problems by modelling the dynamic causal system using a Residual Neural Network
and deriving corresponding counterfactual distributions over trajectories. We
show quantitatively that more root causes are identified when an intervention
is performed on the structural equation and the external influence, compared to
an intervention on the external influence only. By employing an efficient
approximation to a corresponding Shapley value, we also obtain a ranking
between the different subsystems at different points in time being responsible
for an observed failure, which is applicable in settings with large number of
variables. We illustrate the effectiveness of the proposed method on a
benchmark dynamic system as well as on a real world river dataset.

通过使用残差神经网络对动态因果系统进行建模，并得出相应的反事实轨迹分布，我们解决了现有因果方法在确定根本原因时的问题，该方法对静态环境有限且注重导致故障的外部影响，而非结构影响。我们提出的方法在动态系统基准和真实世界河流数据集上具有有效性。

基于反事実的动态系统根本原因分析

Counterfactual-based Root Cause Analysis for Dynamical Systems

This paper presents an approach for identifying the root causes of collective
anomalies given observational time series and an acyclic summary causal graph
which depicts an abstraction of causal relations present in a dynamic system at
its normal regime. The paper first shows how the problem of root cause
identification can be divided into many independent subproblems by grouping
related anomalies using d-separation. Further, it shows how, under this
setting, some root causes can be found directly from the graph and from the
time of appearance of anomalies. Finally, it shows, how the rest of the root
causes can be found by comparing direct causal effects in the normal and in the
anomalous regime. To this end, temporal adaptations of the back-door and the
single-door criterions are introduced. Extensive experiments conducted on both
simulated and real-world datasets demonstrate the effectiveness of the proposed
method.

这篇论文提出了一种通过观察时间序列和一个描述正常状态下动态系统中因果关系的无环摘要因果图来识别集体异常的根本原因的方法，并利用 d 分离将问题分解到多个独立子问题中，并介绍了如何通过比较正常状态下的直接因果效应和异常状态下的直接因果效应来找到其余的根本原因。