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Open AccessArticle

Causal Graphs and Concept-Mapping Assumptions

by 1,* and 2
User-Supplied Information, University at Buffalo, 12 Capen Hall, Buffalo, New York, NY 14260-1660, USA
Martin School, Economics, University of Kentucky, Lexington, KY 40506, USA
Author to whom correspondence should be addressed.
Appl. Syst. Innov. 2018, 1(3), 25;
Received: 11 May 2018 / Revised: 11 July 2018 / Accepted: 20 July 2018 / Published: 24 July 2018
(This article belongs to the Special Issue Fuzzy Decision Making and Soft Computing Applications)
Determining what constitutes a causal relationship between two or more concepts, and how to infer causation, are fundamental concepts in statistics and all the sciences. Causation becomes especially difficult in the social sciences where there is a myriad of different factors that are not always easily observed or measured that directly or indirectly influence the dynamic relationships between independent variables and dependent variables. This paper proposes a procedure for helping researchers explicitly understand what their underlying assumptions are, what kind of data and methodology are needed to understand a given relationship, and how to develop explicit assumptions with clear alternatives, such that researchers can then apply a process of probabilistic elimination. The procedure borrows from Pearl’s concept of “causal diagrams” and concept mapping to create a repeatable, step-by-step process for systematically researching complex relationships and, more generally, complex systems. The significance of this methodology is that it can help researchers determine what is more probably accurate and what is less probably accurate in a comprehensive fashion for complex phenomena. This can help resolve many of our current and future political and policy debates by eliminating that which has no evidence in support of it, and that which has evidence against it, from the pool of what can be permitted in research and debates. By defining and streamlining a process for inferring truth in a way that is graspable by human cognition, we can begin to have more productive and effective discussions around political and policy questions. View Full-Text
Keywords: causality; statistics; concept-mapping; causal graph causality; statistics; concept-mapping; causal graph
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MDPI and ACS Style

Levine, E.; Butler, J.S. Causal Graphs and Concept-Mapping Assumptions. Appl. Syst. Innov. 2018, 1, 25.

AMA Style

Levine E, Butler JS. Causal Graphs and Concept-Mapping Assumptions. Applied System Innovation. 2018; 1(3):25.

Chicago/Turabian Style

Levine, Eli; Butler, J. S. 2018. "Causal Graphs and Concept-Mapping Assumptions" Appl. Syst. Innov. 1, no. 3: 25.

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