Nonlinear independent component analysis (ICA) aims to recover the underlying
independent latent sources from their observable nonlinear mixtures. How to make the …

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 …

Learning graphical conditional independence structures is an important machine learning
problem and a cornerstone of causal discovery. However, the accuracy and execution time …

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 …

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 …

In the causal inference framework of Spirtes, Glymour, and Scheines (SGS), inferences
about causal relationships are made from samples from probability distributions and a …

Within the causal modeling literature, debates about the Causal Faithfulness Condition
(CFC) have concerned whether it is probable that the parameters in causal models will have …

Recent approaches to causal modelling rely upon the causal Markov condition, which
specifies which probability distributions are compatible with a directed acyclic graph (DAG) …

One of the core assumptions in causal discovery is the faithfulness assumption—ie
assuming that independencies found in the data are due to separations in the true causal …