Causal Inference Layer Integration in Hybrid AI Reasoning Engines

Abstract

Hybrid AI reasoning engines are increasingly used in scientific computing to support model
interpretation, hypothesis testing, and exploratory analysis. However, without an explicit causal
inference layer, these systems tend to rely on correlation-based patterns that do not reliably generalize
across perturbations, parameter shifts, or evolving system conditions. This study evaluates the
integration of a causal reasoning layer into a hybrid inference architecture combining symbolic rules,
predictive models, and structured knowledge representations. Results show that the causal layer
improves interpretability, stabilizes reasoning under noisy or high-dimensional scientific data, and
produces more coherent backward-inference explanations while introducing only moderate
computational overhead. The findings demonstrate that causal inference is not merely an enhancement
but a foundational component for trustworthy scientific AI reasoning.