Statistical Causality from a Decision-Theoretic Perspective

TL;DR

This paper reviews the decision-theoretic approach to statistical causality, highlighting its advantages and applications in areas like confounding, treatment effects, and dynamic strategies, and contrasting it with other models.

Contribution

It provides a comprehensive overview of the decision-theoretic framework for causality, clarifying its relation to existing models and illustrating its applicability to complex causal problems.

Findings

Clarifies the decision-theoretic approach to causality

Contrasts with structural equation models and potential responses

Applies to confounding, treatment effects, and dynamic strategies

Abstract

We present an overview of the decision-theoretic framework of statistical causality, which is well-suited for formulating and solving problems of determining the effects of applied causes. The approach is described in detail, and is related to and contrasted with other current formulations, such as structural equation models and potential responses. Topics and applications covered include confounding, the effect of treatment on the treated, instrumental variables, and dynamic treatment strategies.