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Bayesian networks improve causal environmental assessments for evidence-based policy
Citation:
Carriger, J., M. Barron, AND M. Newman. Bayesian networks improve causal environmental assessments for evidence-based policy. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, 50(24):13195-13205, (2016).
Impact/Purpose:
This article highlights opportunities for using Bayesian tools to synthesize evidence during ecological risk assessment. This work demonstrates how probabilistic and causal reasoning about complicated problems are accommodated in BNs and discusses how a causal BN approach would benefit future evidence synthesis in environmental management and risk assessment by improving inferences under high uncertainty. First, the mathematical properties of Bayes theorem for updating beliefs about a hypothesis based on evidence are introduced. The discussion is then broadened to environmental measurements and how Bayes theorem can be utilized to incorporate the explanatory potential of an experiment and its implications for a risk-based hypothesis. A methodology is introduced that examines accuracy of multiple observational methods and confidence in a hypothesis derived from observational inference. Lastly, inclusion of interventional inference is discussed for testing causal assumptions and the causal power of interacting stressors in a complex system. The conclusion describes the implications of the inferential properties of BNs and several remaining issues.
Description:
Rule-based weight of evidence approaches to ecological risk assessment may not account for uncertainties and generally lack probabilistic integration of lines of evidence. Bayesian networks allow causal inferences to be made from evidence by including causal knowledge about the problem, using this knowledge with probabilistic calculus to combine multiple lines of evidence, and minimizing biases in predicting or diagnosing causal relationships. Too often, sources of uncertainty in conventional weight of evidence approaches are ignored that can be accounted for with Bayesian networks. Specifying and propagating uncertainties improve the ability of models to incorporate strength of the evidence in the risk management phase of an assessment. Probabilistic inference from a Bayesian network allows evaluation of changes in uncertainty for variables from the evidence. The network structure and probabilistic framework of a Bayesian approach provide advantages over qualitative approaches in weight of evidence for capturing the impacts of multiple sources of quantifiable uncertainty on predictions of ecological risk. Bayesian networks can facilitate the development of evidence-based policy under conditions of uncertainty by incorporating analytical inaccuracies or the implications of imperfect information, structuring and communicating causal issues through qualitative directed graph formulations, and quantitatively comparing the causal power of multiple stressors on valued ecological resources. These aspects are demonstrated through hypothetical problem scenarios that explore some major benefits of using Bayesian networks for reasoning and making inferences in evidence-based policy.
