Causal inference in coupled human and natural systems

• Published in: Proceedings of the National Academy of Sciences - PNAS 2019-03, Vol.116 (12), p.5311-5318
• Main Authors: Ferraro, Paul J., Sanchirico, James N., Smith, Martin D.
• Format: Article
• Language: English
• Subjects: Biological Sciences; COLLOQUIUM PAPERS; Disasters; Ecosystem; Ecosystem disturbance; Environment; Feedback; Human factors; Humans; Inference; Interference; Marine protected areas; Program Evaluation - standards; Protected areas; Research - legislation & jurisprudence; Sackler on Economics, Environment, and Sustainable Development; Social Sciences; Sustainability science
• ISSN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.1805563115

Coupled human and natural systems (CHANS) are complex, dynamic, interconnected systems with feedback across social and environmental dimensions. This feedback leads to formidable challenges for causal inference. Two significant challenges involve assumptions about excludability and the absence of interference. These two assumptions have been largely unexplored in the CHANS literature, but when either is violated, causal inferences from observable data are difficult to interpret. To explore their plausibility, structural knowledge of the system is requisite, as is an explicit recognition that most causal variables in CHANS affect a coupled pairing of environmental and human elements. In a large CHANS literature that evaluates marine protected areas, nearly 200 studies attempt to make causal claims, but few address the excludability assumption. To examine the relevance of interference in CHANS, we develop a stylized simulation of a marine CHANS with shocks that can represent policy interventions, ecological disturbances, and technological disasters. Human and capital mobility in CHANS is both a cause of interference, which biases inferences about causal effects, and a moderator of the causal effects themselves. No perfect solutions exist for satisfying excludability and interference assumptions in CHANS. To elucidate causal relationships in CHANS, multiple approaches will be needed for a given causal question, with the aim of identifying sources of bias in each approach and then triangulating on credible inferences. Within CHANS research, and sustainability science more generally, the path to accumulating an evidence base on causal relationships requires skills and knowledge from many disciplines and effective academic-practitioner collaborations.