Combining Experts’ Causal Judgments

Dalal Alrajeh; Hana Chockler; Joseph Y. Halpern

Combining Experts’ Causal Judgments

Dalal Alrajeh, Hana Chockler, Joseph Y. Halpern

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

4 Citations (Scopus)

181 Downloads (Pure)

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’ 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 combined. We then use it as the basis for combining experts causal judgments. We illustrate our approach on a number of real-life examples.

Original language	English
Title of host publication	Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence
Publisher	AAAI Press
Pages	6311-6318
Number of pages	9
Publication status	Published - 2 Feb 2018
Event	Thirty-Second AAAI Conference on Artificial Intelligence - New Orleans, Lousiana, United States Duration: 2 Feb 2018 → 7 Feb 2018 https://aaai.org/Conferences/AAAI-18/

Conference

Conference	Thirty-Second AAAI Conference on Artificial Intelligence
Abbreviated title	AAAI-18
Country/Territory	United States
City	New Orleans, Lousiana
Period	2/02/2018 → 7/02/2018
Internet address	https://aaai.org/Conferences/AAAI-18/

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Combining Experts’ Causal Judgments_Accepted1February2018_GREEN AAMAccepted author manuscript, 254 KB

Cite this

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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 combined. We then use it as the basis for combining experts causal judgments. We illustrate our approach on a number of real-life examples.",

author = "Dalal Alrajeh and Hana Chockler and Halpern, {Joseph Y.}",

year = "2018",

month = feb,

day = "2",

language = "English",

publisher = "AAAI Press",

pages = "6311--6318",

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note = "Thirty-Second AAAI Conference on Artificial Intelligence, AAAI-18 ; Conference date: 02-02-2018 Through 07-02-2018",

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TY - CHAP

T1 - Combining Experts’ Causal Judgments

AU - Alrajeh, Dalal

AU - Chockler, Hana

AU - Halpern, Joseph Y.

PY - 2018/2/2

Y1 - 2018/2/2

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 combined. We then use it as the basis for combining experts causal judgments. 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 combined. We then use it as the basis for combining experts causal judgments. We illustrate our approach on a number of real-life examples.

M3 - Conference paper

SP - 6311

EP - 6318

BT - Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence

PB - AAAI Press

T2 - Thirty-Second AAAI Conference on Artificial Intelligence

Y2 - 2 February 2018 through 7 February 2018

ER -

Combining Experts’ Causal Judgments

Abstract

Conference

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