ArticleProceedings of the National Academy of Sciences of the United States of America2019
Causal inference in coupled human and natural systems.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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Who cites it
52 citing papers in PubMed.
- Unifying bright spots research for people and nature.Nature ecology & evolution · 2026Review
- Causal direct drivers of greenhouse gas emissions in 40,722 wastewater treatment plants in China.Environmental science and ecotechnology · 2026Article
- Unobserved confounders cannot explain over-crediting in avoided deforestation carbon projects.Nature ecology & evolution · 2026Article
- Distributional effects of marine conservation on coastal livelihoods in Eastern Indonesia.Nature communications · 2026Article
- Learning lessons from over-crediting to ensure additionality in forest carbon credits.Nature communications · 2026Article
- On the potential for strategic behavior in jurisdictional REDD.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
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- Causal machine learning methods for understanding land use and land cover change.Landscape ecology · 2026Review
- Dynamics of an estuarine biotic community captured in high spatio-temporal resolution using metabarcoding.Scientific data · 2025Article
- Causal Effects Versus Causal Mechanisms: Two Traditions With Different Requirements and Contributions Towards Causal Understanding.Ecology letters · 2025Review
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- Evidence-base for urban green-blue infrastructure to support insect diversity.Urban ecosystems · 2025Review
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- Global trends in social-ecological systems and disaster research: Bibliometric insights.Jamba (Potchefstroom, South Africa) · 2025Article
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- Protection efforts have resulted in ~10% of existing fish biomass on coral reefs.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Private management of African protected areas improves wildlife and tourism outcomes but with security concerns in conflict regions.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- The causal revolution in biodiversity conservation.Nature human behaviour · 2024Article
- Changes in planned and unplanned canopy openings are linked in Europe's forests.Nature communications · 2024Article
Corrections and comments
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Authors and funding
3 authors.
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Abstract
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.
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