Combining experts’ causal judgments
- Submitting institution
-
Imperial College of Science, Technology and Medicine
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 4779
- Type
- D - Journal article
- DOI
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10.1016/j.artint.2020.103355
- Title of journal
- Artificial Intelligence
- Article number
- ARTN 103355
- First page
- 1
- Volume
- 288
- Issue
- 10
- ISSN
- 0004-3702
- Open access status
- Compliant
- 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 formal framework for reasoning about causes and effective interventions in the presence of multiple expert opinions, when sufficient data for discovering the overall causal model is unavailable – previously considered impossible (see https://doi.org/10.1086/678044). Extended AAAI’18 paper (https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16381; acceptance rate: 24.6%/3800) and was foundation of Alrajeh’s Co-I in US Minerva Initiative project “The Social Ecology of Radicalization” (https://bit.ly/35khIYj; $1M). Led to invitation to NII Shonan Meeting on Causal Reasoning in Systems (https://shonan.nii.ac.jp/seminars/139/). Collaboration with Halpern (Cornell) and Chockler (KCL) was basis for Halpern’s ARO project "Combining and Abstracting Causal Models", $375K. The work has influenced others (https://link.springer.com/chapter/10.1007/978-3-030-14174-5_17, https://ui.adsabs.harvard.edu/abs/2020arXiv200510131F/abstract).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
