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Endogeneity

The condition in which a regressor is correlated with a model's error term — through confounding, simultaneity, or measurement error — so OLS coefficients are biased and inconsistent for the causal effect, collapsing the coefficient's causal reading while leaving its predictive one intact.

Core Idea

Endogeneity is the condition in which a regressor is correlated with a model's error term, so ordinary least squares produces coefficient estimates that are biased and inconsistent for the causal effect. It arises through three structural mechanisms: omitted-variable confounding (an unobserved common cause of regressor and outcome), simultaneity (regressor and outcome co-determine each other), and measurement error in the regressor (attenuating the coefficient toward zero). All three violate exogeneity — the assumption the regressor is set independently of everything else affecting the outcome.

Scope of Application

Because endogeneity is a diagnostic condition on a model-and-data pairing with an attached remedy-menu, it applies wherever causal effects are estimated from a model whose right-hand-side variables may be entangled with the outcome.

  • Labour economics — the textbook case: returns to schooling and the unobserved-ability confound.
  • Industrial organisation — price-quantity simultaneity in demand estimation.
  • Development economics — programme placement endogenous to baseline characteristics.
  • Macroeconomics — policy variables responding to the conditions they explain.
  • Epidemiology — confounding-by-indication, selection-into-treatment, and reverse causation.
  • Political science, sociology, and finance — endogenous take-up, peer effects, and simultaneous determination.

Clarity

Naming endogeneity replaces the coarse slogan "correlation is not causation" with a mechanism — the regressor is correlated with the error — that says exactly how and why a coefficient can describe well yet cause falsely. It collects several pathologies under the violation of exogeneity while keeping them separable, and shifts the burden from "is the coefficient significant?" to "why should this regressor be treated as exogenous?"

Manages Complexity

A double sprawl of biases and repair techniques collapses to a single hinge: is the regressor correlated with the error? The analyst tracks one binary property plus a three-way source classification, and the toolkit becomes a menu indexed by the diagnosed source. The causal warrant, bias direction, and appropriate design all read off the exogeneity condition.

Abstract Reasoning

The construct licenses a boundary-drawing move separating description from causation by the exogeneity condition, a diagnostic move classifying which of three sources broke it, a predictive move on bias sign and shape, an interventionist move mapping source to remedy, and a burden-shifting move forcing the identifying assumption into the open.

Knowledge Transfer

Within causal inference the diagnostic condition and remedy-menu transfer literally across labour economics, IO, development, epidemiology, and finance. The portable lesson — a variable used to explain an outcome may be entangled with it, breaking the causal reading — belongs to the entanglement primes feedback, confounding/common_cause, selection_bias, and measurement_error. The econometric apparatus stays home, and the macroeconomic "endogenous variable" is a distinct homonym.

Relationships to Other Abstractions

Local relationship map for EndogeneityParents appear above the current abstraction, mutual partners to the right, and children below. Node labels state whether each abstraction is prime or domain-specific; colors identify relation types.EndogeneityDOMAINDomain-specific abstraction: Regression — presupposesRegressionDOMAINDomain-specific abstraction: Attenuation Bias — presupposesAttenuation BiasDOMAINDomain-specific abstraction: Instrumental variable — presupposesInstrumentalvariableDOMAINDomain-specific abstraction: Omitted Variable Bias — presupposesOmittedVariable BiasDOMAIN

Current abstraction Endogeneity Domain-specific

Parents (1) — more general patterns this builds on

  • Endogeneity presupposes Regression Domain-specific

    Endogeneity presupposes a regression-style model whose regressor, error term, and exogeneity assumption make the violation definable.

Children (3) — more specific cases that build on this

  • Attenuation Bias Domain-specific presupposes Endogeneity

    Classical regressor-measurement attenuation presupposes the endogeneity created when the noisy observed regressor correlates with the composite error.

  • Instrumental variable Domain-specific presupposes Endogeneity

    Instrumental-variable identification presupposes an endogenous treatment whose correlation with the model error destroys the naive causal coefficient.

  • Omitted Variable Bias Domain-specific presupposes Endogeneity

    Omitted-variable bias presupposes endogeneity because its two-condition gate makes an included regressor correlated with the regression error.

Hierarchy paths (13) — routes to 7 parentless roots

Neighborhood in Abstraction Space

Endogeneity sits in a sparse region of the domain-specific corpus (82nd percentile for distinctiveness): few abstractions share its structure, so a faithful description tends to retrieve it precisely.

Family — Unclustered & Miscellaneous (309 abstractions)

Nearest neighbors

Computed from structural-signature embeddings · 2026-07-12