An epidemiologist would not analyze an epidemic as a purely statistical pattern detached from what is known about transmission. If susceptible individuals can become infected and infected individuals can recover or be removed, that knowledge becomes part of the model’s structure.
Compartmental models such as SIR (susceptible, infected, recovered) and SEIR (susceptible, exposed, infected, recovered) formalize those transitions. Statistical methods remain essential for estimating parameters and testing fit. But the analysis does not begin from a blank slate; it begins from established causal structure.
Finance can draw a similar lesson. Where durable mechanisms are reasonably well understood, they should be represented explicitly. If leverage amplifies forced selling, refinancing conditions shape default risk, inventories influence pricing power, passive flows affect demand, or network structures transmit distress, these are more than recurring correlations. They are mechanisms that can be modeled, tested, and challenged.
Dynamic models can be especially useful here. A regression captures co-movement; a dynamic model represents stocks, flows, delays, and feedback. In finance, that may mean balance-sheet capacity, funding conditions, capital flows, or adoption dynamics. Such models help clarify how the state of the system evolves and how today’s conditions shape tomorrow’s outcomes.