Causal modeling approximations in the medical domain

Studies in the health sciences often seek to discover cause-effect relationships among observed variables of interest, for example: treatments, exposures, preconditions, and outcomes. Consequently, causal modeling and causal discovery are central to medical science. In order to algorithmically consi...

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Bibliographic Details
Main Author: Mazlack, Lawrence J.
Format: Conference Proceeding
Language:English
Subjects:
Automobiles
causal
Cognition
cognitive map
Correlation
Fuels
Glass
Ignition
modeling
Switches
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Summary:Studies in the health sciences often seek to discover cause-effect relationships among observed variables of interest, for example: treatments, exposures, preconditions, and outcomes. Consequently, causal modeling and causal discovery are central to medical science. In order to algorithmically consider causal relations, the relations must be placed into a representation that supports manipulation and discovery. Knowledge of at least some causal effects is inherently imprecise or approximate. The most widespread causal representation is directed acyclic graphs (DAGs). However, DAGs are limited in what they can represent. Another graph methodology, fuzzy cognitive maps (FCMs) hold promise as a model that overcomes some of the difficulties found in other approaches. This paper considers causality and suggests fuzzy cognitive maps as a useful causal representation methodology.
ISSN:1098-7584
DOI:10.1109/FUZZY.2011.6007701