Attribution as a Conditional Reasoning Step in Multifactorial Risk Analysis and Decision-Making

Decision-making in multifactorial risk domains requires prioritization among interacting contributors, yet attribution-the assignment of interpretative weight to contributing factors-remains under-formalized and prone to false precision. While statistical models quantify associations and causal inference clarifies effects, neither directly supports how contributions should be weighed for action under uncertainty. This perspective proposes the FAIR framework (Framing factors, Assembling causal representations, Interpreting diagnostics, Reasoned attribution), which conceptualizes attribution as a conditional reasoning step rather than a statistical output. FAIR integrates explicit factor boundaries, domain-informed causal representations, diagnostics of separability and uncertainty, and decision-oriented interpretation and explicitly identifies situations in which attribution should be avoided in favor of system-level responses. Across illustrative risk contexts, the framework highlights recurrent attribution pitfalls-such as multicollinearity and uncertainty dominance-and clarifies when factor-level attribution can responsibly support governance, prioritization, and decision-making, versus when precautionary or systemic approaches are more appropriate. By positioning attribution as epistemically conditional and action-oriented, FAIR strengthens the connection between risk analysis and decision-making, reducing over-interpretation while supporting transparent and responsible governance in complex risk systems.