The Pretence of Precision: Endogeneity and the Epistemological Limits of Observational Econometrics

Standard econometric pedagogy rests heavily on a single, unverifiable assumption: that observed variables have zero covariance with the unobservable error term (Cov(X, �) =0) Practitioners routinely mistake the mechanical orthogonality of OLS residuals for a profound truth about the unseen universe, creating a dangerous illusion of precision. However, because human economies operate as complex systems of spontaneous order, omitted variable bias is not merely a technical glitch to be solved. It is the inescapable default of deeply entangled data. When subjected to logical scrutiny, the standard mathematical defences built to salvage causal inference, such as Instrumental Variables, Fixed Effects, and sensitivity bounds, quickly collapse. These defences function as epistemological displacements rather than solutions, largely ignoring the problem of induction and the infinite variance inherent to fat-tailed distributions. Ultimately, utilizing these endogenous models to optimize point estimates does not reduce uncertainty; it manufactures a dangerous illusion of precision. We argue that true statistical rigor requires an epistemology of via negativa. Quantitative methods must be repurposed to bound extreme exposures and identify structural fragility, rather than manufacturing a false sense of causal certainty