Time-aware Neuro-symbolic Abduction with Structured Conformal Prediction for Biomedical Causal Discovery

Unraveling deep mechanistic pathways underlying disease progression and drug action is a core pursuit of modern pharmacology and targeted therapeutics. However, transforming sparse and asynchronous clinical observations into statistically verifiable causal structures remains a long-standing challenge.Existing paradigms relying heavily on static knowledge graphs or large language models (LLMs) suffer from critical limitations: structural hallucinations due to insufficient causal constraints, search explosion in open-domain literature mining, rapid knowledge obsolescence for dynamic clinical scenarios, and the absence of distribution-free statistical safety bounds for causal predictions.In this paper, we introduce TRACE, a unified time-aware neuro-symbolic abductive framework integrated with structured conformal prediction, which transforms the passive candidate path validation to active open-domain causal discovery. Concretely, a time-aware hierarchical topological representation module that aligns sparse and asynchronous clinical observations into explicit causal subgraphs is first designed to resolve the mismatch between static knowledge representations and dynamic clinical data. To mitigate search explosion and semantic inconsistency in open-domain evidence retrieval, we propose an active state-graph backward search algorithm to guide multi-hop causal chain assembly across heterogeneous literature streams.Then, we incorporate macro-skill discovery and temporal update mechanism with continual learning objectives, which reduces online computational overhead and mitigates knowledge aging by continuously distilling verified reasoning trajectories.Furthermore, we establish a structured conformal prediction module tailored for multi-hop directed acyclic graphs, which defines path-level joint non-conformity scores via atomic triple decoupling and constructs distribution-free confidence bounds with finite-sample coverage guarantees, addressing the lack of statistical reliability in existing causal path predictions.We validate TRACE across four benchmark datasets: MIMIC, ADE-Time, CoMAGC, and FB15k-Subset. Experimental results demonstrate that TRACE achieves an absolute MRR gain of up to 10.4% over state-of-the-art baselines, while strictly maintaining the target 95% statistical coverage rate guaranteed by the conformal prediction module, reducing average prediction set sizes by over 50.0% via tighter confidence bounds, and accelerating inference speed by 54% through macro-skill reuse.