Effective root cause analysis (RCA) is vital for swiftly restoring services,
minimizing losses, and ensuring the smooth operation and management of complex
systems. Previous data-driven RCA methods, particularly those employing causal
discovery techniques, have primarily focused on constructing dependency or
causal graphs for backtracking the root causes. However, these methods often
fall short as they rely solely on data from a single modality, thereby
resulting in suboptimal solutions. In this work, we propose Mulan, a unified
multi-modal causal structure learning method for root cause localization. We
leverage a log-tailored language model to facilitate log representation
learning, converting log sequences into time-series data. To explore intricate
relationships across different modalities, we propose a contrastive
learning-based approach to extract modality-invariant and modality-specific
representations within a shared latent space. Additionally, we introduce a
novel key performance indicator-aware attention mechanism for assessing
modality reliability and co-learning a final causal graph. Finally, we employ
random walk with restart to simulate system fault propagation and identify
potential root causes. Extensive experiments on three real-world datasets
validate the effectiveness of our proposed framework.

本研究提出了一种统一的多模态因果结构学习方法 Mulna，通过一个定制的语言模型来学习日志表示，将日志序列转化为时间序列数据，并采用基于对比学习的方法提取多模态中不变和特定于模态的表示。此外，引入了一种考虑关键性能指标的注意机制来评估模态可靠性，并共同学习最终的因果图。最后，通过随机游走重启来模拟系统故障传播并识别潜在的根本原因。对三个真实世界数据集的广泛实验证实了我们提出的框架的有效性。

多模态因果结构学习与根本原因分析

Multi-modal Causal Structure Learning and Root Cause Analysis

The discovery of causal relationships in a set of random variables is a
fundamental objective of science and has also recently been argued as being an
essential component towards real machine intelligence. One class of causal
discovery techniques are founded based on the argument that there are inherent
structural asymmetries between the causal and anti-causal direction which could
be leveraged in determining the direction of causation. To go about capturing
these discrepancies between cause and effect remains to be a challenge and many
current state-of-the-art algorithms propose to compare the norms of the kernel
mean embeddings of the conditional distributions. In this work, we argue that
such approaches based on RKHS embeddings are insufficient in capturing
principal markers of cause-effect asymmetry involving higher-order structural
variabilities of the conditional distributions. We propose Kernel Intrinsic
Invariance Measure with Heterogeneous Transform (KIIM-HT) which introduces a
novel score measure based on heterogeneous transformation of RKHS embeddings to
extract relevant higher-order moments of the conditional densities for causal
discovery. Inference is made via comparing the score of each hypothetical
cause-effect direction. Tests and comparisons on a synthetic dataset, a
two-dimensional synthetic dataset and the real-world benchmark dataset
T\"ubingen Cause-Effect Pairs verify our approach. In addition, we conduct a
sensitivity analysis to the regularization parameter to faithfully compare
previous work to our method and an experiment with trials on varied
hyperparameter values to showcase the robustness of our algorithm.

基于异构转换的内核固有不变量测量（KIIM-HT）提出了一种基于 RKHS 嵌入的新型评分测量方法，以提取条件密度的相关高阶矩以用于因果关系的发现。