Value of information analysis for interventional and counterfactual Bayesian networks in forensic medical sciences

Constantinou A. C., YET B., Fenton N., Neil M., Marsh W.

ARTIFICIAL INTELLIGENCE IN MEDICINE, vol.66, pp.41-52, 2016 (SCI-Expanded, Scopus) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 66
  • Publication Date: 2016
  • Doi Number: 10.1016/j.artmed.2015.09.002
  • Journal Name: ARTIFICIAL INTELLIGENCE IN MEDICINE
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.41-52
  • Keywords: Causal inference, Bayesian networks, Interventional analysis, Counterfactual analysis, Value of Information, Forensic medicine, PARTIAL EXPECTED VALUE, CLINICAL-TRIAL DESIGN, SENSITIVITY-ANALYSIS, SAMPLE-SIZE, RISK
  • Middle East Technical University Affiliated: No

Abstract

Objectives: Inspired by real-world examples from the forensic medical sciences domain, we seek to determine whether a decision about an interventional action could be subject to amendments on the basis of some incomplete information within the model, and whether it would be worthwhile for the decision maker to seek further information prior to suggesting a decision.