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 …

It is commonplace to encounter heterogeneous or nonstationary data, of which the
underlying generating process changes across domains or over time. Such a distribution …

This article presents an overview of several known approaches to causal discovery. It is
organized by relating the different fundamental assumptions that the methods depend on …

Directed acyclic graphical models are widely used to represent complex causal systems.
Since the basic task of learning such a model from data is NP-hard, a standard approach is …

Researchers in explainable artificial intelligence have developed numerous methods for
helping users understand the predictions of complex supervised learning models. By …

We consider the problem of learning a Bayesian network or directed acyclic graph model
from observational data. A number of constraint‐based, score‐based and hybrid algorithms …

Causal models are mathematical models representing causal relationships within an
individual system or population. They facilitate inferences about causal relationships from …

This paper considers a new identifiability condition for additive noise models (ANMs) in
which each variable is determined by an arbitrary Borel measurable function of its parents …

Even when the causal graph underlying our data is unknown, we can use observational
data to narrow down the possible values that an average treatment effect (ATE) can take by …

Causality has been a burgeoning field of research leading to the point where the literature
abounds with different components addressing distinct parts of causality. For researchers, it …