Unraveling complex causal processes that affect sustainability requires more integration between empirical and modeling approaches

Scientists seek to understand the causal processes that generate sustainability problems and determine effective solutions. Yet, causal inquiry in nature-society systems is hampered by conceptual and methodological challenges that arise from nature-society interdependencies and the complex dynamics they create. Here, we demonstrate how sustainability scientists can address these challenges and make more robust causal claims through better integration between empirical analyses and process-or agent-based modeling. To illustrate how these different epistemological traditions can be integrated, we present four studies of air pollution regulation, natural resource management, and the spread of COVID-19. The studies show how integration can improve empirical estimates of causal effects, inform future research designs and data collection, enhance understanding of the complex dynamics that underlie observed temporal patterns, and elucidate causal mechanisms and the contexts in which they operate. These advances in causal understanding can help sustainability scientists develop better theories of phenomena where social and ecological processes are dynamically intertwined and prior causal knowledge and data are limited. The improved causal understanding also enhances governance by helping scientists and practitioners choose among potential interventions, decide when and how the timing of an intervention matters, and anticipate unexpected outcomes. Methodological integration, however, requires skills and efforts of all involved to learn how members of the respective other tradition think and analyze nature-society systems. Significance Understanding causation in nature-society systems is fundamental to advancing sustainability science and addressing sustainability problems, such as climate change, pollution, natural resource collapse, and the spread of infectious diseases. To study causation, sustainability scientists rely on modeling or on empirical analyses but rarely combine the two approaches. This lack of integration is problematic because nature-society interdependencies and the complex dynamics they create pose conceptual and methodological challenges that cannot be addressed by each approach in isolation. Using four studies, we demonstrate how integration between empirical analyses and modeling can reduce uncertainty in empirical estimates, improve the design of empirical analyses, uncover causal mechanisms, enhance understanding of the drivers of complex temporal patterns, and elucidate unexpected outcomes.