The second edition of this bestseller provides a practical and accessible introduction to the
main concepts, foundation, and applications of Bayesian networks. This edition contains a …

Causal discovery becomes especially challenging when the possibility of latent confounding
and/or selection bias is not assumed away. For this task, ancestral graph models are …

The biological and social sciences often generalize causal conclusions from one context or
location to others that may differ in some relevant respects, as is illustrated by inferences …

Philosophy of Science: A Unified Approach combines a general introduction to philosophy of
science with an integrated survey of all its important subfields. As the book's subtitle …

We show how a generic feature-selection algorithm returning strongly relevant variables can
be turned into a causal structure-learning algorithm. We prove this under the Faithfulness …

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

Much of the recent work on the epistemology of causation has centered on two assumptions,
known as the Causal Markov Condition and the Causal Faithfulness Condition …

We start this paper by arguing that causality should, in analogy with force in Newtonian
physics, be understood as a theoretical concept that is not explicated by a single definition …

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 …

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