title: Value of information analysis for interventional and counterfactual Bayesian networks in forensic medical sciences.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  The method is based on the underlying principle of Value of Information to enhance decision analysis in interventional and counterfactual Bayesian networks.
  
  
  RESULTS
  The method is applied to two real-world Bayesian network models (previously developed for decision support in forensic medical sciences) to examine the average gain in terms of both Value of Information (average relative gain ranging from 11.45% and 59.91%) and decision making (potential amendments in decision making ranging from 0% to 86.8%).
  
  
  CONCLUSIONS
  We have shown how the method becomes useful for decision makers, not only when decision making is subject to amendments on the basis of some unknown risk factors, but also when it is not. Knowing that a decision outcome is independent of one or more unknown risk factors saves us from the trouble of seeking information about the particular set of risk factors. Further, we have also extended the assessment of this implication to the counterfactual case and demonstrated how answers about interventional actions are expected to change when some unknown factors become known, and how useful this becomes in forensic medical science.
authors.affiliation: Risk and Information Management Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Road, Mile End Campus, Computer Science Building, E1 4NS London, UK. Electronic address: anthony@constantinou.info.
authors.forename: Anthony Costa
authors.initials: AC
authors.lastname: Constantinou
authors.affiliation: Risk and Information Management Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Road, Mile End Campus, Computer Science Building, E1 4NS London, UK.
authors.forename: Barbaros
authors.initials: B
authors.lastname: Yet
authors.affiliation: Risk and Information Management Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Road, Mile End Campus, Computer Science Building, E1 4NS London, UK.
authors.forename: Norman
authors.initials: N
authors.lastname: Fenton
authors.affiliation: Risk and Information Management Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Road, Mile End Campus, Computer Science Building, E1 4NS London, UK.
authors.forename: Martin
authors.initials: M
authors.lastname: Neil
authors.affiliation: Risk and Information Management Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Road, Mile End Campus, Computer Science Building, E1 4NS London, UK.
authors.forename: William
authors.initials: W
authors.lastname: Marsh
mesh_terms: D001499:Bayes Theorem; D015984:Causality; D002755:Choice Behavior; D003198:Computer Simulation; D003661:Decision Support Techniques; D005554:Forensic Medicine / Q000379:methods*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D015233:Models, Statistical; D016571:Neural Networks, Computer; D018570:Risk Assessment; D012307:Risk Factors; D035501:Uncertainty; D014754:Violence / Q000208:ethnology / Q000517:prevention & control / Q000523:psychology*
keywords: Bayesian networks; Causal inference; Counterfactual analysis; Forensic medicine; Interventional analysis; Value of Information