What question did this study set out to answer?

The aim is to develop and validate a framework for establishing causality in AI systems without relying on prior causal knowledge.

May 25, 2026Open Access

COREX: Causal Origin Resolution and Empirical eXamination — An Autonomous Multi-Stage Framework for Robust Causal Discrimination in Data-Driven AI Systems

Key Points

The aim is to develop and validate a framework for establishing causality in AI systems without relying on prior causal knowledge.
Introduced a deterministic, graph-free, model-agnostic framework for causal analysis.
Implemented a four-axis evaluation pipeline: Statistical Stability, Representation Invariance, Intervention Consistency, and Domain Robustness.
Validated the framework using 1,500 synthetic datasets and real-world electrophysiology data.
Achieved a C-Precision of 0.91, C-Recall of 0.88, and A-Recall of 0.93, with a False Causal Rate of 0.07.
Successfully classified causal and spurious relationships with a scoring system categorizing outcomes as CAUSAL, SPURIOUS, or REPRESENTATION ARTIFACT.

Abstract

COREX (Causal Origin Resolution and Empirical eXamination) is a deterministic, graph-free, model-agnostic computational framework that treats causality as an empirically testable robustness property rather than an assumed structural characteristic. The framework implements a four-axis evaluation pipeline — Statistical Stability (S), Representation Invariance (R), Intervention Consistency (I), and Domain Robustness (D) — and fuses their outputs through a weighted scoring function to produce a calibrated causal classification: CAUSAL (≥ 0.80), SPURIOUS (0.50–0.79), or REPRESENTATION ARTIFACT (< 0.50). Unlike prior approaches dependent on pre-specified causal graphs or structural equation models, COREX requires no prior causal knowledge and operates as a meta-evaluation layer atop any learned relationship. An optional learnable MLP meta-scoring layer provides adaptive calibration for high-dimensional settings. Validated on 1,500 synthetic labeled datasets and real-world vagus nerve electrophysiology data: C-Precision 0.91 · C-Recall 0.88 · A-Recall 0.93 · False Causal Rate 0.07. Released as open-source Python package (PyPI: pip install corex) under MIT License. OSF Preregistration: https://doi.org/10.17605/OSF.IO/3ABZF | BIO-MED-02 | Ronin Institute / Rite of Renaissance.

COREX: Causal Origin Resolution and Empirical eXamination — An Autonomous Multi-Stage Framework for Robust Causal Discrimination in Data-Driven AI Systems

Key Points

Abstract

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