Probabilistic Reasoning in Intelligent Systems is a complete andaccessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty.Expand

1. Introduction to probabilities, graphs, and causal models 2. A theory of inferred causation 3. Causal diagrams and the identification of causal effects 4. Actions, plans, and direct effects 5.… Expand

Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty.Expand

This paper extends network-based methods of constraint satisfaction to include continuous variables, thus providing a framework for processing temporal constraints that permits the processing of metric information, namely, assessments of time differences between events.Expand

This paper presents a new definition of path-specific effects that is based on path switching, instead of vari able fixing, and that extends the interpretation and evaluation of direct and indirect effects to nonlinear models.Expand

SUMMARY The primary aim of this paper is to show how graphical models can be used as a mathematical language for integrating statistical and subject-matter information. In particular, the paper… Expand

A canonical representation for causal models is presented which yields an efficient graphical crite rion for deciding equivalence, and provides a theoret ical basis for extracting causal structures from em pirical data.Expand

Belief networks are directed acyclic graphs in which the nodes represent propositions (or variables), the arcs signify direct dependencies between the linked propositions, and the strengths of these dependencies are quantified by conditional probabilities.Expand

This review presents empiricalresearcherswith recent advances in causal inference, and stresses the paradigmatic shifts that must be un- dertaken in moving from traditionalstatistical analysis to… Expand