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Combining Experts’ Causal Judgments

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Combining Experts’ Causal Judgments. / Alrajeh, Dalal; Chockler, Hana; Halpern, Joseph Y.

In: ARTIFICIAL INTELLIGENCE, Vol. 288, 103355, 11.2020.

Research output: Contribution to journalArticle

Harvard

Alrajeh, D, Chockler, H & Halpern, JY 2020, 'Combining Experts’ Causal Judgments', ARTIFICIAL INTELLIGENCE, vol. 288, 103355. https://doi.org/10.1016/j.artint.2020.103355

APA

Alrajeh, D., Chockler, H., & Halpern, J. Y. (2020). Combining Experts’ Causal Judgments. ARTIFICIAL INTELLIGENCE, 288, [103355]. https://doi.org/10.1016/j.artint.2020.103355

Vancouver

Alrajeh D, Chockler H, Halpern JY. Combining Experts’ Causal Judgments. ARTIFICIAL INTELLIGENCE. 2020 Nov;288. 103355. https://doi.org/10.1016/j.artint.2020.103355

Author

Alrajeh, Dalal ; Chockler, Hana ; Halpern, Joseph Y. / Combining Experts’ Causal Judgments. In: ARTIFICIAL INTELLIGENCE. 2020 ; Vol. 288.

Bibtex Download

@article{53fad1c71fef43aba3f9d424f9247e22,
title = "Combining Experts{\textquoteright} Causal Judgments",
abstract = "Consider a policymaker who wants to decide which intervention to perform in order to change a currently undesirable situation. The policymaker has at her disposal a team of experts, each with their own understanding of the causal dependencies between different factors contributing to the outcome. The policymaker has varying degrees of confidence in the experts{\textquoteright} opinions. She wants to combine their opinions in order to decide on the most effective intervention. We formally define the notion of an effective intervention, and then consider how experts{\textquoteright} causal judgments can be combined in order to determine the most effective intervention. We define a notion of two causal models being compatible, and show how compatible causal models can be merged. We then use it as the basis for combining experts{\textquoteright} causal judgments. We also provide a definition of decomposition for causal models to cater for cases when models are incompatible. We illustrate our approach on a number of real-life examples.",
keywords = "Causality, Combining causal judgments, Complexity, Intervention",
author = "Dalal Alrajeh and Hana Chockler and Halpern, {Joseph Y.}",
year = "2020",
month = nov,
doi = "10.1016/j.artint.2020.103355",
language = "English",
volume = "288",
journal = "ARTIFICIAL INTELLIGENCE",
issn = "0004-3702",
publisher = "Elsevier",

}

RIS (suitable for import to EndNote) Download

TY - JOUR

T1 - Combining Experts’ Causal Judgments

AU - Alrajeh, Dalal

AU - Chockler, Hana

AU - Halpern, Joseph Y.

PY - 2020/11

Y1 - 2020/11

N2 - Consider a policymaker who wants to decide which intervention to perform in order to change a currently undesirable situation. The policymaker has at her disposal a team of experts, each with their own understanding of the causal dependencies between different factors contributing to the outcome. The policymaker has varying degrees of confidence in the experts’ opinions. She wants to combine their opinions in order to decide on the most effective intervention. We formally define the notion of an effective intervention, and then consider how experts’ causal judgments can be combined in order to determine the most effective intervention. We define a notion of two causal models being compatible, and show how compatible causal models can be merged. We then use it as the basis for combining experts’ causal judgments. We also provide a definition of decomposition for causal models to cater for cases when models are incompatible. We illustrate our approach on a number of real-life examples.

AB - Consider a policymaker who wants to decide which intervention to perform in order to change a currently undesirable situation. The policymaker has at her disposal a team of experts, each with their own understanding of the causal dependencies between different factors contributing to the outcome. The policymaker has varying degrees of confidence in the experts’ opinions. She wants to combine their opinions in order to decide on the most effective intervention. We formally define the notion of an effective intervention, and then consider how experts’ causal judgments can be combined in order to determine the most effective intervention. We define a notion of two causal models being compatible, and show how compatible causal models can be merged. We then use it as the basis for combining experts’ causal judgments. We also provide a definition of decomposition for causal models to cater for cases when models are incompatible. We illustrate our approach on a number of real-life examples.

KW - Causality

KW - Combining causal judgments

KW - Complexity

KW - Intervention

UR - http://www.scopus.com/inward/record.url?scp=85088228685&partnerID=8YFLogxK

U2 - 10.1016/j.artint.2020.103355

DO - 10.1016/j.artint.2020.103355

M3 - Article

VL - 288

JO - ARTIFICIAL INTELLIGENCE

JF - ARTIFICIAL INTELLIGENCE

SN - 0004-3702

M1 - 103355

ER -

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