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 …

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

Video causal reasoning aims to achieve a high-level understanding of video content from a
causal perspective. However, current video reasoning tasks are limited in scope, primarily …

Abstract Directed Acyclic Graphs (DAGs) provide an efficient framework to describe the
causal relations in actual applications, and it appears more and more important to learn a …

The fusion of causal models with deep learning introducing increasingly intricate data sets,
such as the causal associations within images or between textual components, has surfaced …

Reinforcement learning (RL) and causal modelling naturally complement each other. The
goal of causal modelling is to predict the effects of interventions in an environment, while the …

Discovering causal relationships from observational data is a challenging topic in artificial
intelligence. Recent works formulate causal discovery as a continuous optimization problem …

We introduce DAG-TFRC, a novel method for learning directed acyclic graphs (DAGs) from
time series with few root causes. By this, we mean that the data are generated by a small …

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