Developing a Language for Applied Causal Analysis: The Assessment of Causal Networks in Interdisciplinary Research

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2018

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Integration of disparate research fields has become a major concern in recent years due to the increasing complexity of the issues that face policy makers and researchers. Concerted efforts have therefore been initiated to remove the traditional barriers between research fields to allow for greater cooperation between policy makers and researchers, particularly in the fields of health, the environment, and development. The Bridge Collaborative is one such organization dedicated to facilitating this process through the use of results chains. However, because of a lack of experimental data or observational datasets traditionally endemic to interdisciplinary policy research, they lack an effective mechanism for analyzing causal dependence among network variables. The purpose of this thesis is therefore to create a method of analyzing causal relationships using expert knowledge that can still pass the rigorous tests necessary to assert causality in the traditional experimental and observational data approaches. Building upon previous work of statisticians, philosophers, and computer scientists, I create a question template that will allow a researcher to easily check and refine a causal network and explore alternatives to that network based on experience and elicited expert judgement alone. I then perform a case study using this template based on the work of the Food-Energy-Water (FEW) Catalyst project, a group initiative within the Bridge Collaborative, to review a causal network based on a systematic literature search. I conclude that a causal network can indeed be constructed, explored, and adjusted using logical reasoning and expert judgement—a finding that has implications for researchers seeking to create reliable models using causal networks as their base.

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Turpin, Kyle Joseph (2018). Developing a Language for Applied Causal Analysis: The Assessment of Causal Networks in Interdisciplinary Research. Master's thesis, Duke University. Retrieved from https://hdl.handle.net/10161/18290.

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