Interpretability and explainability are crucial for machine learning (ML) and statistical
applications in medicine, economics, law, and natural sciences and form an essential …

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 …

This paper examines the relationship among CO2 emissions, energy use, and GDP in
Russia using annual data ranging from 1990 to 2020. We first conduct time-series analyses …

With the increasing demands for accountability, interpretability is becoming an essential
capability for real-world AI applications. However, most methods utilize post-hoc approaches …

We propose causal recurrent variational autoencoder (CR-VAE), a novel generative model
that is able to learn a Granger causal graph from a multivariate time series x and …

The ability to understand causality from data is one of the major milestones of human-level
intelligence. Causal Discovery (CD) algorithms can identify the cause-effect relationships …

Causal Discovery (CD) is the process of identifying the cause-effect relationships among the
variables from data. Over the years, several methods have been developed primarily based …

Deep learning models developed for time-series associated tasks have become more
widely researched nowadays. However, due to the unintuitive nature of time-series data, the …

Temporal data representing chronological observations of complex systems can be
ubiquitously collected in smart industry, medicine, finance and etc. In the last decade, many …

This paper considers learning the hidden causal network of a linear networked dynamical
system (NDS) from the time series data at some of its nodes--partial observability. The …