mechanismsfrom
Mechanismsfrom is a term used to describe a methodological framework for deriving causal mechanisms from observed data. Introduced in the early 2020s by interdisciplinary research teams, mechanismsfrom aims to produce modular, mechanistic explanations rather than purely descriptive or correlational models. The central idea is to infer latent mechanisms—such as feedback loops, regulatory modules, or physical constraints—that could generate observed phenomena while remaining grounded in domain knowledge.
Mechanismsfrom treats explanations as compositions of interacting components. It blends data-driven inference with prior constraints drawn
Applications span fields such as systems biology, economics, social science, and engineering. In biology, mechanismsfrom can
Critics note challenges in identifiability, where multiple mechanisms may explain the same data, and in ensuring
See also: mechanistic models, causal inference, abductive reasoning, data-driven modeling.