Abstract Bayesian Networks (BNs) have become increasingly popular over the last few
decades as a tool for reasoning under uncertainty in fields as diverse as medicine, biology …

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 …

Causal disentanglement aims to uncover a representation of data using latent variables that
are interrelated through a causal model. Such a representation is identifiable if the latent …

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 …

This paper demonstrates how to recover causal graphs from the score of the data
distribution in non-linear additive (Gaussian) noise models. Using score matching …

Learning graphical structure based on Directed Acyclic Graphs (DAGs) is a challenging
problem, partly owing to the large search space of possible graphs. A recent line of work …

There has been an increasing interest in methods that exploit permutation reasoning to
search for directed acyclic causal models, including the “Ordering Search''of Teyssier and …

Discovering causal relations from observational data becomes possible with additional
assumptions such as considering the functional relations to be constrained as nonlinear with …

Sequential experimental design to discover interventions that achieve a desired outcome is
a key problem in various domains including science, engineering and public policy. When …

Recently, an intriguing class of non-convex optimization problems has emerged in the
context of learning directed acyclic graphs (DAGs). These problems involve minimizing a …