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 …

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 …

Recently, structure learning of directed acyclic graphs (DAGs) has been formulated as a
continuous optimization problem by leveraging an algebraic characterization of acyclicity …

This paper investigates in which cases continuous optimization for directed acyclic graph
(DAG) structure learning can and cannot perform well and why this happens, and suggests …

Time series data are found in many areas of healthcare such as medical time series,
electronic health records (EHR), measurements of vitals, and wearable devices. Causal …

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 …

In online advertising, marketing mix modeling (MMM) is employed to predict the gross
merchandise volume (GMV) of brand shops and help decision-makers to adjust the budget …

Despite significant research, discovering causal relationships from fMRI remains a
challenge. Popular methods such as Granger Causality and Dynamic Causal Modeling fall …

Causal learning from data has received much attention in recent years. One way of
capturing causal relationships is by utilizing Bayesian networks. There, one recovers a …