Physics is a field of science that has traditionally used the scientific method to answer
questions about why natural phenomena occur and to make testable models that explain the …

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 …

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 …

A concise and self-contained introduction to causal inference, increasingly important in data
science and machine learning. The mathematization of causality is a relatively recent …

We describe two modifications that parallelize and reorganize caching in the well-known
Greedy Equivalence Search algorithm for discovering directed acyclic graphs on random …

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 …

There is great interest in estimating brain “networks” from FMRI data. This is often attempted
by identifying a set of functional “nodes”(eg, spatial ROIs or ICA maps) and then conducting …

A general framework for constructing and using probabilistic models of complex systems that
would enable a computer to use available information for making decisions. Most tasks …

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 …

Written by one of the preeminent researchers in the field, this book provides a
comprehensive exposition of modern analysis of causation. It shows how causality has …