2025/11/02 by Thomas F Heston, Heston, Thomas F.
Computer Science · Mathematics · #62G35 #Advanced Causal Inference Techniques #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Other Statistics (stat.OT) #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.2511.00982
openalex publication_date 2025/11/02 · openalex created_date 2025/11/06 · openalex updated_date 2026/07/28
We introduce the Neutrality Boundary Framework (NBF), a set of geometric metrics for quantifying statistical robustness and fragility as the normalized distance from the neutrality boundary, the manifold where the effect equals zero. The neutrality boundary value nb in [0,1) provides a threshold-free, sample-size invariant measure of stability that complements traditional effect sizes and p-values. We derive the general form nb = |Delta - Delta0| / (|Delta - Delta0| + S), where S>0 is a scale parameter for normalization; we prove boundedness and monotonicity, and provide domain-specific implementations: Risk Quotient (binary outcomes), partial eta2 (ANOVA), and Fisher z-based measures (correlation). Unlike threshold-dependent fragility indices, NBF quantifies robustness geometrically across arbitrary significance levels and statistical contexts.