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

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.