Abstract
Medical foundation models generate narrative explanations but cannot quantify intervention effects, detect evidence conflicts, or validate literature claims, limiting clinical auditability. We propose causal compilation, a paradigm that transforms medical evidence from narrative text into executable code. The paradigm standardizes heterogeneous research evidence into structured estimand objects, each explicitly specifying intervention contrast, effect scale, time horizon, and target population, supporting six executable causal queries: do-calculus, counterfactual reasoning, temporal trajectories, heterogeneous effects, mechanistic decomposition, and joint interventions. We instantiate this paradigm in DoAtlas-1, compiling 1,445 effect kernels from 754 studies through effect standardization, conflict-aware graph construction, and real-world validation (Human Phenotype Project, 10,000 participants). The system achieves 98.5% canonicalization accuracy and 80.5% query executability. This paradigm shifts medical AI from text generation to executable, auditable, and verifiable causal reasoning.
Abstract (translated)
医学基础模型可以生成叙述性解释，但无法量化干预效果、检测证据冲突或验证文献声明，这限制了临床审计的可行性。我们提出因果编译（causal compilation）的概念，这是一种将医疗证据从叙述文本转化为可执行代码的范式。该方法通过标准化异质研究证据为结构化的估计对象，每个对象明确指明干预对比、影响规模、时间范围和目标人群，支持六种可执行的因果查询：do-演算（do-calculus）、反事实推理、时间轨迹分析、异质性效应分析、机制分解以及联合干预。我们通过DoAtlas-1实现了这一范式，在这个系统中编译了来自754项研究的1,445个效果内核，这些编译过程包括效果标准化、冲突感知图构建和现实世界验证（人类表型项目，涉及10,000名参与者）。该系统的规范性准确率为98.5%，查询执行率为80.5%。这一范式将医学人工智能从文本生成转向了可执行的、可审计的和可验证的因果推理。
URL
https://arxiv.org/abs/2602.19158
