Abstract
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.
Highlights
Causal inference is a key goal for understanding the relationships among phenomena in the real world that researchers are attempting to study
The hope of this paper is to clarify what is more probably true from what is less probably true and to streamline the pool of evidence that is permissible in policy and political debates
It is possible that some specific aspects of the graphs may change over time with peoples’ attitudes, preferences, and perspectives. It is assumed explicitly in this paper that the underlying method of creating causal graphs with fact nodes and interaction edges can be valid throughout time, space, and context, even if the specific models change over time
Summary
Causal inference is a key goal for understanding the relationships among phenomena in the real world that researchers are attempting to study [1] This becomes a challenging task when possible causal phenomena are numerous, highly interrelated, complex, and complicated to study with validity [2,3]. There is no clear method for either promoting correct facts and high quality and honestly treated evidence, or for eliminating incorrect facts and inferences of poor quality, or dishonestly treated evidence from the pool of knowledge that is acceptable in policy debates. This paper proposes a possible method to clarify researchers’ intentions and work, determine what data are necessary to collect, guide the selection of the methodology of treating the evidence, and produce possible counterfactual arguments that can be tested to establish a greater probability that correct inferences are drawn from the data. High quality and honestly treated evidence gains precedence over, and is promoted in discussions and debates, at the expense of poor quality and dishonestly treated evidence
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