Combining Experts’ Causal Judgments
- Submitting institution
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King's College London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 132302156
- Type
- D - Journal article
- DOI
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10.1016/j.artint.2020.103355
- Title of journal
- ARTIFICIAL INTELLIGENCE
- Article number
- 103355
- First page
- -
- Volume
- 288
- Issue
- -
- ISSN
- 0004-3702
- Open access status
- Exception within 3 months of publication
- Month of publication
- July
- Year of publication
- 2020
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
-
-
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The first paper to propose a framework for combining experts' opinions on models and interventions on top of structural causality. It defines interventions and compatibility for opinion combinations, overcoming impossibility results for combining models [Bradley, Dietrich, List, 2014]. It states and rigorously proves complexity results and algorithms and describes computation of a combined model to guide policy makers towards best interventions. The studied possible real-life applications include combining expert opinions on causes of famine, radicalisation in prisons and child abuse cases (“Baby P” case). Applicability and integration of this framework in police work and legal inquest and inquiry are shown.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -