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。