Decision-theoretic foundations for statistical causality

Decision Theoretic Foundations for Statistical Network Models

Decision - Theoretic Foundations for Causal

We present a deenition of cause and eeect in terms of decision-theoretic primitives and thereby provide a principled foundation for causal reasoning. Our deenition departs from the traditional view of causation in that causal assertions may vary with the set of decisions available. We argue that this approach provides added clarity to the notion of cause. Also in this paper, we examine the enco...

On Decision-Theoretic Foundations for Defaults

In recent years, considerable effort has gone into understanding default reasoning. Most of this effort concentrated on the question of entailment, i.e., what conclusions are warranted by a knowledge-base of defaults. Surprisingly, few works formally examine the general role of defaults. We argue that an examination of this role is necessary in order to understand defaults, and suggest a concre...

Decision-Theoretic Foundations for Causal Reasoning

We present a de nition of cause and e ect in terms of decision-theoretic primitives and thereby provide a principled foundation for causal reasoning. Our de nition departs from the traditional view of causation in that causal assertions may vary with the set of decisions available. We argue that this approach provides added clarity to the notion of cause. Also in this paper, we examine the enco...

Logical Foundations for Decision-Theoretic Planning

Decision-theoretic planning attempts to combine the resources of classical AI planning theory and contemporary decision theory. The basic idea is that plans are assigned expected values, and the planning agent chooses between competing plans in terms of those expected values. This complicates the search for plans in predictable ways, and the natural inclination of the planning theorist is to tu...