# Causal Graphs and Concept-Mapping Assumptions

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## Abstract

**:**

## 1. Introduction

## 2. Literature Review

## 3. The Methodology

- Draw out the causal graph as the researcher perceives it to be. This is the conceptual mapping stage, since all causal graphs are ultimately concept maps. Nodes or points in the graph are facts or conditions, edges between the nodes are interactions and associations among the facts. The researcher should be free to base this initial step on their own working knowledge, the existing literature on the subject in question, and any applicable theory;
- Consult with other researchers, policymakers, and stakeholders to develop alternative facts and conditions and alternative ways for them to interact with each other through the interaction edges in the graph;
- Design research projects to test the validity of the factual nodes and interaction edges that are produced from Steps 1 and 2:
- a
- It is important to note that this paper is agnostic about the specifics of the designs of the research, so long as it is logically valid and testable;
- b
- This is where any number of qualitative and quantitative methods can be used;
- c
- It is also a good idea to use multiple methods on the same factual node or interaction edge to increase the probability of validity. That is often called robustness in research;

- Out of the population of causal graphs that were created, assign equal probabilities that each one is valid based on the total number of causal graphs that are explored.
- a
- The probability of the population of causal graphs can never truly equal 1 for complete validity because there is always an unknown quantity of potential error present in the population of models, i.e., the unknown unknowns;
- b
- The probabilities can be explicitly Bayesian, empirical Bayesian, or based on flat priors;

- Consider the quality and source of the evidence that is presented. If quality evidence for a particular edge or node is present, then that adds to the probability that that edge or node is true at the expense of other edges and nodes. If there is evidence against a node or edge, it subtracts from the probability that that edge or node is true without necessarily affecting alternative edges and nodes. Poorer quality evidence has less of an effect, or no effect on the probability of demonstrating truth;
- Alter the probability of validity for each of the graphs as evidence becomes apparent through new research. This can be based on Bayesian updating or frequentist testing;
- Repeat Steps 1 through 6 using a variety of techniques to examine each node and each edge in the causal graph.

_{3}and y

_{1}. Other models can be constructed using all of the possibilities. For simplicity’s sake, most of these options in the research design space have been left out. However, if the researcher(s) are able to get the largest possible collection of causal graphs together while staying relevant to the topic(s) at hand, the larger design space should provide a rich environment for testing the factual assumptions and interactions among the variables. Researchers can then work together across disciplines to design experiments and determine which data to collect and how in order to “shave away” at the hypothesis space of the research topic. The surviving causal graphs, which withstand the scrutiny of the researchers’ efforts, can be said to be more probably true and valid than the other causal graphs that have aspects that are not valid or which have little to no evidence in support of them. These surviving causal graphs correspond to Bayesian posteriors or unrejected frequentist hypotheses, in that they are the end products of analyses.

## 4. Implications of this Method for Policy Research

## 5. Caveats to this Method

## 6. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Holland, P.W. Statistics and causal inference. J. Am. Stat. Assoc.
**1986**, 81, 945–960. [Google Scholar] [CrossRef] - Dennard, L.F.; Richardson, K.A.; Morcol, G. Complexity and Policy Analysis: Tools and Concepts for Designing Robust Policies in a Complex World; ISCE Pub: Goodyear, AZ, USA, 2008. [Google Scholar]
- Sayama, H. Introduction to the Modeling and Analysis of Complex Systems; Open SUNY Textbooks; Milne Library: Geneseo, NY, USA, 2015. [Google Scholar]
- Merriam-Webster Online Dictionary. Available online: http://www.merriam-webster.com/dictionary/causality (accessed on 2 May 2016).
- Bennett, A.; Elman, C. Complex causal relations and case study methods: The example of path dependence. Polit. Anal.
**2006**, 14, 250–267. [Google Scholar] [CrossRef] - Blalock, H.M. (Ed.) Causal Models in the Social Sciences; Transaction Publishers: New York, NY, USA, 1985. [Google Scholar]
- Higgins, E.T.; Kruglanski, A.W. (Eds.) Motivated social cognition: Principles of the interface. In Social Psychology: Handbook of Basic Principles; Guildford Press: New York, NY, USA, 1996; pp. 493–520. ISBN 9781572301009. [Google Scholar]
- Holland, P.W. Causal inference, path analysis and recursive structural equations models. ETS Res. Rep. Ser.
**1988**, 1988, i-50. [Google Scholar] [CrossRef] - Robins, J.M.; Hernán, M.Á.; Brumback, B. Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology
**2000**, 11, 550–560. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Helmert, M.; Richter, S. Fast downward-making use of causal dependencies in the problem representation. In Proceedings of the International Planning Competition, Hosted at the 14th International Conference on Automated Planning and Scheduling (IPC4, ICAPS 2004), Whistler, BC, Canada, 3–7 June 2004; pp. 41–43. [Google Scholar]
- Galea, S.; Riddle, M.; Kaplan, G.A. Causal thinking and complex system approaches in epidemiology. Int. J. Epidemiol.
**2010**, 39, 97–106. [Google Scholar] [CrossRef] [PubMed] - Plowright, R.K.; Sokolow, S.H.; Gorman, M.E.; Daszak, P.; Foley, J.E. Causal inference in disease ecology: Investigating ecological drivers of disease emergence. Front. Ecol. Environ.
**2008**, 6, 420–429. [Google Scholar] [CrossRef] - Granger, C.W. Testing for causality: A personal viewpoint. J. Econ. Dyn. Control
**1980**, 2, 329–352. [Google Scholar] [CrossRef] - Granger, C.W. Some recent development in a concept of causality. J. Econom.
**1988**, 39, 199–211. [Google Scholar] [CrossRef] - King, G.; Keohane, R.O.; Verba, S. Designing Social Inquiry: Scientific Inference in Qualitative Research; Princeton University Press: Princeton, NJ, USA, 1994. [Google Scholar]
- Pierce, D.A.; Haugh, L.D. Causality in temporal systems: Characterization and a survey. J. Econom.
**1977**, 5, 265–293. [Google Scholar] [CrossRef] - Sobel, M.E. Causal inference in the social and behavioral sciences. In Handbook of Statistical Modeling for the Social and Behavioral Sciences; Springer: New York, NY, USA, 1995; pp. 1–38. ISBN 978-0306448058. [Google Scholar]
- Eichler, M. Granger causality and path diagrams for multivariate time series. J. Econom.
**2007**, 137, 334–353. [Google Scholar] [CrossRef] - Pearl, J. Causal Inference in Statistics: An overview. Stat. Surv.
**2009**, 3, 96–146. [Google Scholar] [CrossRef] - Trochim, W.M. An introduction to concept mapping for planning and evaluation. Eval. Progr. Plan.
**1989**, 12, 1–16. [Google Scholar] [CrossRef] - Chen, H.; Giménez, O. Causal graphs and structurally restricted planning. J. Comput. Syst. Sci.
**2010**, 76, 579–592. [Google Scholar] [CrossRef] - Kiiveri, H.; Speed, T.P.; Carlin, J.B. Recursive causal models. J. Aust. Math. Soc.
**1984**, 36, 30–52. [Google Scholar] [CrossRef] - Kosko, B. Fuzzy cognitive maps. Int. J. Man-Mach. Stud.
**1986**, 24, 65–75. [Google Scholar] [CrossRef] - Helmert, M. A Planning Heuristic Based on Causal Graph Analysis. In Proceedings of the 14th International Conference on Automated Planning and Scheduling, Whistler, BC, Canada, 3–7 June 2004; pp. 161–170. [Google Scholar]
- Helmert, M.; Geffner, H. Unifying the Causal Graph and Additive Heuristics. In Proceedings of the 18th International Conference on Automated Planning and Scheduling, Sydney, Australia, 14–18 September 2008; pp. 140–147. [Google Scholar]
- Morgan, M.S. The History of Econometric Ideas; Cambridge University Press: Cambridge, UK, 1990. [Google Scholar]
- Berard, C. Group Model Building Using System Dynamics: An Analysis of Methodological Frameworks. J. Bus. Res.
**2010**, 8, 13–24. [Google Scholar] - Hovmand, P. Community Based System Dynamics; Springer: New York, NY, USA, 2014. [Google Scholar]
- Vennix, J.A.M.; Akkermans, H.A.; Rouwette, E.A.J.A. Group model-building to facilitate organizational change: An exploratory study. Syst. Dyn. Rev.
**1996**, 12, 39–58. [Google Scholar] [CrossRef] - Balcetis, E. Where the Motivation Resides and Self-Deception Hides: How Motivated Cognition Accomplishes Self-Deception. Soc. Personal. Psychol. Compass
**2008**, 2, 361–381. [Google Scholar] [CrossRef] - Baumeister, R.F. Self-regulation and ego threat: Motivated cognition, self deception, and destructive goal setting. In The Psychology of Action: Linking Cognition and Motivation to Behavior; Gollwitzer, P.M., Bargh, J.A., Eds.; Guilford Press: New York, NY, USA, 1996; pp. 27–47. [Google Scholar]
- Jost, J.T.; Glaser, J.; Kruglanski, A.W.; Sulloway, F.J. Political conservatism as motivated social cognition. Psychol. Bull.
**2003**, 129, 339–375. [Google Scholar] [CrossRef] [PubMed] - Chown, E.; Kaplan, S.; Kortenkamp, D. Prototypes, location, and associative networks (PLAN): Towards a unified theory of cognitive mapping. Cogn. Sci.
**1995**, 19, 1–51. [Google Scholar] [CrossRef] - Eden, C. On the nature of cognitive maps. J. Manag. Stud.
**1992**, 29, 261–265. [Google Scholar] [CrossRef] - Ennis, R.H. Identifying implicit assumptions. Synthese
**1982**, 51, 61–86. [Google Scholar] [CrossRef] - Kitchin, R.; Freundschuh, S. Cognitive Mapping: Past, Present, and Future; Routledge: London, UK, 2000. [Google Scholar]
- Özesmi, U.; Özesmi, S.L. Ecological models based on people’s knowledge: A multi-step fuzzy cognitive mapping approach. Ecol. Model.
**2004**, 176, 43–64. [Google Scholar] [CrossRef] - Axelrod, R.M. Structure of Decision: The Cognitive Maps of Political Elites; Princeton University Press: Princeton, NJ, USA, 1976. [Google Scholar]
- Nadkarni, S.; Shenoy, P.P. A Bayesian network approach to making inferences in causal maps. Eur. J. Oper. Res.
**2001**, 128, 479–498. [Google Scholar] [CrossRef] [Green Version] - Nadkarni, S.; Shenoy, P.P. A causal mapping approach to constructing Bayesian networks. Dec. Support Syst.
**2004**, 38, 259–281. [Google Scholar] [CrossRef] [Green Version] - Siau, K.; Tan, X. Improving the quality of conceptual modeling using cognitive mapping techniques. Data Knowl. Eng.
**2005**, 55, 343–365. [Google Scholar] [CrossRef] - Swan, J. Using cognitive mapping in management research: Decisions about technical innovation. Br. J. Manag.
**1997**, 8, 183–198. [Google Scholar] [CrossRef] - Korver, M.; Lucas, P.J. Converting a rule-based expert system into a belief network. Med. Inform.
**1993**, 18, 219–241. [Google Scholar] [CrossRef] [Green Version] - McCawley, P.F. The Logic Model for Program Planning and Evaluation; University of Idaho Extension: Moscow, ID, USA, 2010; CLS 1097; pp. 1–5. Available online: https://www.researchgate.net/publication/237568681_The_Logic_Model_for_Program_Planning_and_Evaluation (accessed on 11 June 2018).
- Jost, J.T.; Amodio, D.M. Political ideology as motivated social cognition: Behavioral and neuroscientific evidence. Motiv. Emot.
**2012**, 36, 55–64. [Google Scholar] [CrossRef] - Jost, J.T.; Glaser, J.; Kruglanski, A.W.; Sulloway, F.J. Exceptions that prove the rule—Using a theory of motivated social cognition to account for ideological incongruities and political anomalies: Reply to Greenberg and Jonas (2003). Psychol. Bull.
**2003**, 129, 383–393. [Google Scholar] [CrossRef]

**Figure 2.**An alternative graph to Figure 1.

**Figure 3.**An alternative graph to Figure 2.

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Levine, E.; Butler, J.S.
Causal Graphs and Concept-Mapping Assumptions. *Appl. Syst. Innov.* **2018**, *1*, 25.
https://doi.org/10.3390/asi1030025

**AMA Style**

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

Levine, Eli, and J. S. Butler.
2018. "Causal Graphs and Concept-Mapping Assumptions" *Applied System Innovation* 1, no. 3: 25.
https://doi.org/10.3390/asi1030025