A Decision-Based View of Causality

TL;DR

This paper integrates causal modeling with decision analysis by defining causal dependence in decision terms and introducing causal influence diagrams to better represent cause-effect relationships for decision-making.

Contribution

It introduces causal influence diagrams, a new class of influence diagrams that accurately represent causal dependence in decision contexts, addressing limitations of traditional models.

Findings

Causal dependence can be represented within influence diagrams.

Causal influence diagrams correct key inadequacies of ordinary influence diagrams.

Relationships between Howard Canonical Form and graphical cause representations are clarified.

Abstract

Most traditional models of uncertainty have focused on the associational relationship among variables as captured by conditional dependence. In order to successfully manage intelligent systems for decision making, however, we must be able to predict the effects of actions. In this paper, we attempt to unite two branches of research that address such predictions: causal modeling and decision analysis. First, we provide a definition of causal dependence in decision-analytic terms, which we derive from consequences of causal dependence cited in the literature. Using this definition, we show how causal dependence can be represented within an influence diagram. In particular, we identify two inadequacies of an ordinary influence diagram as a representation for cause. We introduce a special class of influence diagrams, called causal influence diagrams, which corrects one of these problems, and identify situations where the other inadequacy can be eliminated. In addition, we describe the relationships between Howard Canonical Form and existing graphical representations of cause.