Determinanters

Determinanters are a term used in theoretical models across disciplines to denote factors that determine the state or outcome of a system. They are similar to determinants but are framed as active drivers within a model, whose particular states constrain the set of possible outcomes. The term is a neologism in the literature and is not yet standardized, but is used to emphasize causal roles that go beyond correlation.

A determinanter can be any variable, condition, or structure that, under given rules, makes certain results possible while ruling out others. Direct determinanters influence outcomes in a single causal step, whereas indirect determinanters shape the effect of other drivers or the arrangement of the system, often through mediating pathways or feedback loops.

Identifying determinanters typically involves causal analysis, such as structural equation modeling, causal graphs, or counterfactual reasoning, together with sensitivity to context and model assumptions. Quantification may use effect sizes, probability shifts, or counterfactual comparisons.

Applications are found in social science, epidemiology, engineering, and policy analysis, where knowing determinanters helps prioritize interventions. Examples: vaccination coverage as a determinanter for disease spread; access to education as a determinanter of learning outcomes; regulatory regime as a determinanter of market behavior.

Limitations include context dependence, potential for multiple interacting determinanters, and the risk of overemphasizing a subset of drivers. The term should be used with care, clearly defining what counts as a determinanter within a given model and acknowledging potential alternative explanations.

In sum, determinanters offer a framing device for causal analysis that foregrounds drivers with sufficient explanatory power to determine outcomes within specified assumptions.