Temporal causal discovery is a crucial task aimed at uncovering the causal
relations within time series data. The latest temporal causal discovery methods
usually train deep learning models on prediction tasks to uncover the causality
between time series. They capture causal relations by analyzing the parameters
of some components of the trained models, e.g., attention weights and
convolution weights. However, this is an incomplete mapping process from the
model parameters to the causality and fails to investigate the other
components, e.g., fully connected layers and activation functions, that are
also significant for causal discovery. To facilitate the utilization of the
whole deep learning models in temporal causal discovery, we proposed an
interpretable transformer-based causal discovery model termed CausalFormer,
which consists of the causality-aware transformer and the decomposition-based
causality detector. The causality-aware transformer learns the causal
representation of time series data using a prediction task with the designed
multi-kernel causal convolution which aggregates each input time series along
the temporal dimension under the temporal priority constraint. Then, the
decomposition-based causality detector interprets the global structure of the
trained causality-aware transformer with the proposed regression relevance
propagation to identify potential causal relations and finally construct the
causal graph. Experiments on synthetic, simulated, and real datasets
demonstrate the state-of-the-art performance of CausalFormer on discovering
temporal causality. Our code is available at
this https URL

通过预测任务和设计的多核因果卷积，CausalFormer 学习时序数据的因果表达，并通过回归相关传播解释训练后的模型全局结构以构建因果图，从而在发现时序因果性方面实现了最先进的性能。

因果 Transformer: 时间因果发现的可解释 Transformer

CausalFormer: An Interpretable Transformer for Temporal Causal Discovery

In the field of Artificial Intelligence for Information Technology
Operations, causal discovery is pivotal for operation and maintenance of graph
construction, facilitating downstream industrial tasks such as root cause
analysis. Temporal causal discovery, as an emerging method, aims to identify
temporal causal relationships between variables directly from observations by
utilizing interventional data. However, existing methods mainly focus on
synthetic datasets with heavy reliance on intervention targets and ignore the
textual information hidden in real-world systems, failing to conduct causal
discovery for real industrial scenarios. To tackle this problem, in this paper
we propose to investigate temporal causal discovery in industrial scenarios,
which faces two critical challenges: 1) how to discover causal relationships
without the interventional targets that are costly to obtain in practice, and
2) how to discover causal relations via leveraging the textual information in
systems which can be complex yet abundant in industrial contexts. To address
these challenges, we propose the RealTCD framework, which is able to leverage
domain knowledge to discover temporal causal relationships without
interventional targets. Specifically, we first develop a score-based temporal
causal discovery method capable of discovering causal relations for root cause
analysis without relying on interventional targets through strategic masking
and regularization. Furthermore, by employing Large Language Models (LLMs) to
handle texts and integrate domain knowledge, we introduce LLM-guided
meta-initialization to extract the meta-knowledge from textual information
hidden in systems to boost the quality of discovery. We conduct extensive
experiments on simulation and real-world datasets to show the superiority of
our proposed RealTCD framework over existing baselines in discovering temporal
causal structures.

提出了一种能在工业场景中发现时间因果关系的 RealTCD 框架，通过遮蔽和正则化的策略，采用基于得分的时间因果发现方法，无需依赖干预目标进行根本原因分析，并结合大型语言模型和领域知识来提取系统中隐藏的文本信息中的元知识，以提高发现质量。在模拟和真实数据集上进行了大量实验证明了 RealTCD 框架相对于现有基准的优越性。

LLM 在干预数据的时间域因果推断中的增强

LLM-Enhanced Causal Discovery in Temporal Domain from Interventional  Data

Conventional temporal causal discovery (CD) methods suffer from high
dimensionality, fail to identify lagged causal relationships, and often ignore
dynamics in relations. In this study, we present a novel constraint-based CD
approach for autocorrelated and non-stationary time series data (eCDANs)
capable of detecting lagged and contemporaneous causal relationships along with
temporal changes. eCDANs addresses high dimensionality by optimizing the
conditioning sets while conducting conditional independence (CI) tests and
identifies the changes in causal relations by introducing a surrogate variable
to represent time dependency. Experiments on synthetic and real-world data show
that eCDANs can identify time influence and outperform the baselines.

提出了一种新的约束条件因果推断方法 eCDANs，旨在解决传统时态因果推断方法的问题，具有检测迟滞和同时因果关系以及时间变化的能力。实验表明该方法能够识别时间影响并优于基线方法。