Causal graphs and concept-mapping assumptions

Eli Levine, J. S. Butler

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish
Article number25
Pages (from-to)1-10
Number of pages10
JournalApplied System Innovation
Issue number3
StatePublished - Sep 2018

Bibliographical note

Publisher Copyright:
© 2018 by the authors. Licensee MDPI, Basel, Switzerland.


  • Causal graph
  • Causality
  • Concept-mapping
  • Statistics

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering
  • Applied Mathematics
  • Artificial Intelligence
  • Human-Computer Interaction
  • Information Systems


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