At the core of causal inference lies the challenge of determining reliable causal graphs
solely based on observational data. Since the well-known backdoor criterion depends on …

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 …

Causality in an industrial process provides insights into how various process variables
interact and affect each other within the system. It reveals the underlying mechanisms of …

Identification and appropriate handling of inconsistencies in data at deployment time is
crucial to reliably use machine learning models. While recent data-centric methods are able …

Causal learning has attracted much attention in recent years because causality reveals the
essential relationship between things and indicates how the world progresses. However …

Learning causal relationships solely from observational data provides insufficient
information about the underlying causal mechanism and the search space of possible …

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 …

Causal discovery is the challenging task of inferring causal structure from data. Motivated by
Pearl's Causal Hierarchy (PCH), which tells us that passive observations alone are not …

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 …