Abstract for: In Pursuit of Evidence-Based Policy: The Adoption of Causal Modeling Approaches

Public decision-makers require a sufficient evidentiary base to ensure positive outcomes from policy action. They tend to assume that, if only they were armed with more and better data, then they could better ensure such outcomes. As data is not information, so too, without benefit of developed evidence that adequately represents a sufficient causal or complex systems view, they lack adequate vision into the context and preferable solution(s). The preponderance of traditional agency-collected decision-making information is not sufficiently curated and comes in the form of linear, reduced, or correlational data. Thus, policy makers are forced to commit known fallacy by reasoning, without adequate evidence, from correlation to causation. Jay Forrester, the father of Systems Dynamics, gave shape and voice to these issues in his shocking findings on the efficacy of Urban Planning in 1969 (Forrester, 1969). In this presentation, the authors propose a hybrid approach to resolve the drawbacks of current evidence-building practices. Our goals are: 1) to popularize and highlight both the criticality and the utility of a developed causal modeling approach and its robust applicability to multiple complex domains; and 2) invite constructive feedback as to how to better overcome social or technical barriers to adoption.