Understanding the laws that govern a phenomenon is the core of scientific progress. This is
especially true when the goal is to model the interplay between different aspects in a causal …

Fault diagnosis is crucial for realizing safe process operation when a fault occurs.
Multivariate statistical process control (MSPC) has widely been adopted for fault detection in …

Existing causal discovery methods based on combinatorial optimization or search are slow,
prohibiting their application on large-scale datasets. In response, more recent methods …

The development of data science has been needed in environmental fields such as marine,
weather, and soil data. In general, the datasets are large in some cases, but they are often …

Causal discovery identifies causal relationships in data, but the task is more complex for
multivariate time series due to the computational demands of methods like VarLiNGAM …

A crucial step in remedying faults within network infrastructure is to determine their root
cause. However, the large-scale, complex and dynamic nature of modern architecture …

Learning the causal structures from observational data is an omnipresent challenge in data
science. The amount of observational data available to Causal Structure Learning (CSL) …

This chapter discusses estimation methods for the coefficient matrix B of the basic LiNGAM
model introduced in Chap. 2. There are two main approaches. One estimation approach …

Learning causal models involves extracting meaningful relationships and dependencies
between variables from observational or experimental data. This process often employs …