Neuro-Symbolic Active Causal Hypothesis Testing for NAD+-Centered Alzheimer's Disease Reversal

Large language models (LLMs) generate fluent scientific narratives but frequently produce unfalsifiable, mechanistically inconsistent causal claims—a critical failure mode in biomedical reasoning. We introduce Active Causal Hypothesis Testing (ACHT), a neuro-symbolic framework that integrates LLM agents for hypothesis generation, differentiable causal discovery for structure learning, and symbolic verification for mechanistic constraint enforcement. We evaluate ACHT on the biologically grounded task of NAD+-centered Alzheimer’s disease (AD) reversal, leveraging recent demonstrations of pharmacologic reversal of advanced AD phenotypes via NAD+ homeostasis restoration. In retrospective evaluation against a 12-node, 16-edge ground-truth causal graph encoding established NAD+/AD biology, ACHT achieves an edge F1 of 0.90, satisfies 6/6 mechanistic constraints, and correctly predicts all 8 directional outcomes of P7C3-A20 intervention. In prospective ODE simulation, ACHT’s Bayesian active selection converges to lower structural Hamming distance than random or entropy-based baselines. Ablation reveals that removing LLM priors degrades F1 by 0.29 (from 0.84 to 0.55), while removing symbolic verification reduces constraint satisfaction by 16% (relative). Our results demonstrate that verifiable, constraint-aware reasoning—not narrative plausibility—should be the standard for AI-driven scientific hypothesis generation.