In this review, we discuss approaches for learning causal structure from data, also called
causal discovery. In particular, we focus on approaches for learning directed acyclic graphs …

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 …

Background Learning the causal structure helps identify risk factors, disease mechanisms,
and candidate therapeutics for complex diseases. However, although complex biological …

Among network security issues, distributed denial of service (DDoS) attacks are particularly
harmful to a network. Several previous machine learning (ML)-based network intrusion …

We consider the problem of structure learning for linear causal models based on
observational data. We treat models given by possibly cyclic mixed graphs, which allow for …

Generalizing Bayesian networks, maximal ancestral graphs (MAGs) are a theoretically
appealing model class for dealing with unobserved variables. Despite significant advances …

Causal reasoning in relational domains is fundamental to studying real-world social
phenomena in which individual units can influence each other's traits and behavior …

Causal discovery amounts to unearthing causal relationships amongst features in data. It is
a crucial companion to causal inference, necessary to build scientific knowledge without …

We propose a new optimization-based method for learning causal structures from
observational data, a process known as causal discovery. Our method takes as input …

La representación del conocimiento de un estudiante en un área disciplinar juega un rol
importante para impulsar sus habilidades. Para apoyar a los involucrados en el ámbito …