Understanding causal interactions between brain activity and metabolic processes is a central challenge in systems neuroscience and psychiatric research. This chapter reviews current data-driven approaches for inferring causal relationships in complex interacting systems, with a particular focus on the metabolismbrain nexus in psychopathology. Widely used methods (e.g., Granger causality, transfer entropy, dynamic causal modeling) are critically assessed and their theoretical underpinnings, limitations, and applicability to neuronal and metabolic data are discussed. Special attention is given to recent methodological developments including structural causal models, simulation-based inference, and models tailored to systems with feedback loops, such as the Bicycle framework. Using major depressive disorder as a case study, the need for integrated modeling frameworks is highlighted that combine biophysical realism with statistical rigor. It is argued that mechanistic computational models, when combined with experimental interventions, hold the greatest promise for uncovering true causal mechanisms in complex biological systems.