🇨🇦A Causal Framework for Analyzing Cumulative Environmental Impacts

Steven F. Wilson, Ph.D., EcoLogic Research

Presented at the 10th Annual BayesiaLab Conference on Monday, October 24, 2022.

Abstract Significant environmental degradation is rarely the result of a single, acute event but is more often caused by the additive and/or synergistic effects of several stressors. Assessing these “cumulative impacts” is an important component of environmental assessments, but the procedures for calculating such impacts are theoretically weak and could generate misleading estimates of project impacts. Here, I propose a framework for analyzing cumulative environmental impacts that is rooted in causal theory. Specifically, I argue for the application of causal models and the explicit incorporation of “rung three” counterfactual reasoning from Pearl’s causal hierarchy. The important but underused concepts of necessary and sufficient causation are prominent in the proposed framework and lead to surprising assessment results when used to estimate the effects of industrial development on grizzly bear and caribou populations.

Presentation Video

Presentation Slides

About the Presenter

Steven F. Wilson, Ph.D., EcoLogic Research, 302-99 Chapel Street, Nanaimo, BC V9R 5H3, Canada, [e-mail]

Steve Wilson has 30 years of experience working at technical and professional levels in strategic and operational planning for wildlife and other ecological values. He specializes in quantitative approaches to decision support and policy analysis. Steve holds a Ph.D. in wildlife ecology from the University of British Columbia in Vancouver.

Steven Wilson, Ph.D.

Previous Conference Presentations

🇨🇦pageUsing Bayesian Networks to Map Winter Habitat for Mountain Goats in Coastal British Columbia🇨🇦pageApplication of Bayesian Belief Networks to Evidence-Based Policy Development🇨🇦pageThe Small Data Problem: Using Bayesian Networks in Endangered Species Policy Development🇨🇦pageUsing Bayesian Networks to Characterize Wildlife Habitat Use

• The Social Graph—Using Bayesian Networks to Identify Spatial Population Structuring Among Caribou in Subarctic Canada (Paris, 2017)

• Use of causal modeling with Bayesian networks to inform policy options for sustainable resource management (Nashville, 2016)

• Big data, small data: Bayesian networks in environmental policy analysis in Canada’s energy sector (Fairfax, 2015)