A causal mapping approach to constructing Bayesian networks
By Sucheta Nadkarni, Prakash P. Shenoy. Decision Support Systems, 2004
334 citations recorded by the source corpus.
This paper describes a systematic procedure for constructing Bayesian networks (BNs) from domain knowledge of experts using the causal mapping approach. We outline how causal knowledge of experts can be represented as causal maps, and how the graphical structure of causal maps can be modified to construct Bayes nets. Probability encoding techniques can be used to assess the numerical parameters of the resulting Bayes nets. We illustrate the construction of a Bayes net starting from a causal map of a systems analyst in the context of an information technology application outsourcing decision.
Subfields: Artificial Intelligence, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/21ab2a173308d7a53d486fc6c6c01b7cec63e104
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Rob D'hondt
- Kalyanmoy Deb
- Guy Engelen
- Ralph E. Steuer
- Jyrki Wallenius
- Joris Aertsens
- Steven Broekx
- Dries Landuyt
In Management Science and Operations Research
- Kalyanmoy Deb
- Ralph E. Steuer
- Nicolas Huck
- Yaochu Jin
- Jafar Rezaei
- Matteo Brunelli
- Ran Cheng
- Xing-yi Zhang
Citations in the corpus
- Multiple Criteria Decision Making, Multiattribute Utility Theory: Recent Accomplishments and What Lies Ahead (2008, methodology): “For situations with discrete levels of both attributes and objectives, Nadkarni and Shenoy (2001, 2004) provide an assessment procedure using a causal mapping approach to constructing Bayesian networks.”
- A review of Bayesian belief networks in ecosystem service modelling (2013, background): “The resulting causal map forms the basis for developing an operational BBN (Nadkarni and Shenoy, 2004).”