Data ScienceHigh Confidence

Confounding, Specification, and Model Drift

Why model validity silently decays in deployed predictive systems.

Models inherit the conditioning of their training context; data drift, specification choice, and confounding interact to create silent validity loss. The LHS Lens treats deployed models as inheritance-and-conditioning systems operating at the population scale; the discipline is to monitor whether the conditioning has shifted under the deployed model.

confoundingspecificationmodel driftSimpson's paradox

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TCS — Truth Coherence System50
BCI — Belief Coherence Index50
PCM — Pattern Coherence Metric50
RAS — Regulation Activation Score50
IIR — Integration Integrity Ratio50
RER — Recovery Efficiency Rate50
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An AI-powered research library organized through the LHS Lens for inherited conditioning analysis. The LHS Lens is an analytical framework — it does not assert conclusions as fact. Always verify evidence from primary sources.

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