Statistically sound pattern discovery harnesses the rigour of statistical hypothesis testing to
overcome many of the issues that have hampered standard data mining approaches to …

Discovering causal relationships from observational data is a crucial problem and it has
applications in many research areas. The PC algorithm is the state-of-the-art constraint …

Causal discovery studies the problem of mining causal relationships between variables from
data, which is of primary interest in science. During the past decades, significant amount of …

Causality analysis continues to remain one of the fundamental research questions and the
ultimate objective for a tremendous amount of scientific studies. In line with the rapid …

Revealing complex relations between entities (eg, items within or between transactions) is of
great significance for business optimization, prediction, and decision making. Such relations …

Both artificial intelligence (AI) and domain knowledge from human experts play an important
role in manufacturing decision making. Smart manufacturing emphasizes a fully automated …

This article elaborates how to identify and evaluate causal factors to improve scientific
impact. Currently, analyzing scientific impact can be beneficial to various academic activities …

In causal inference, estimating heterogeneous treatment effects (HTE) is critical for
identifying how different subgroups respond to interventions, with broad applications in …

Cancer data analysis is significant to detect the codes that are responsible for cancer
diseases. It is significant to find out the coding regions from diseases infected biological …

Discovering causal relationships from genomic data is the ultimate goal in gene regulation
research. Constraint based causal exploration algorithms, such as PC, FCI, RFCI, PC …