Physics is a field of science that has traditionally used the scientific method to answer
questions about why natural phenomena occur and to make testable models that explain the …

Temporal data, representing chronological observations of complex systems, has always
been a typical data structure that can be widely generated by many domains, such as …

Bayesian causal discovery aims to infer the posterior distribution over causal models from
observed data, quantifying epistemic uncertainty and benefiting downstream tasks …

Time-series causal discovery (TSCD) is a fundamental problem of machine learning.
However, existing synthetic datasets cannot properly evaluate or predict the algorithms' …

Learning the causal structure of high-dimensional non-stationary time series can help in
understanding the data generation mechanism, which is a crucial task in machine learning …

Causal learning has garnered significant attention in recent years because it reveals the
essential relationships that underpin phenomena and delineates the mechanisms by which …

There are two issues in news-driven multi-stock movement prediction tasks that are not well
solved in the existing works. On the one hand," relation discovery" is a pivotal part when …

There are two issues in news-driven multi-stock movement prediction tasks that are not well
solved in the existing works. On the one hand," relation discovery" is a pivotal part when …

With the advancement of neural networks, diverse methods for neural Granger causality
have emerged, which demonstrate proficiency in handling complex data, and nonlinear …

Discovering causal relationships from time series data is significant in fields such as finance,
climate science, and neuroscience. However, contemporary techniques rely on the …