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 …

A graphical model is a statistical model that is associated with a graph whose nodes
correspond to variables of interest. The edges of the graph reflect allowed conditional …

We introduce in this survey the major concepts, models, and algorithms proposed so far to
infer causal relations from observational time series, a task usually referred to as causal …

What is the difference between a prediction that is made with a causal model and that with a
non-causal model? Suppose that we intervene on the predictor variables or change the …

Causal inference is essential for data-driven decision making across domains such as
business engagement, medical treatment and policy making. However, research on causal …

We consider constraint-based methods for causal structure learning, such as the PC-, FCI-,
RFCI-and CCD-algorithms (Spirtes et al., 1993, 2000; Richardson, 1996; Colombo et al …

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 …

In this paper we prove the so-called “Meek Conjecture”. In particular, we show that if a DAG
H is an independence map of another DAG G, then there exists a finite sequence of edge …

Bayesian networks are graphical structures for representing the probabilistic relationships
among a large number of variables and doing probabilistic inference with those variables …

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 …