DoAtlas-1: A Causal Compilation Paradigm for Clinical AI

TL;DR

DoAtlas-1 introduces a causal compilation paradigm that converts medical evidence into executable code, enabling structured, auditable causal reasoning in clinical AI.

Contribution

This work presents a novel paradigm and system for transforming heterogeneous medical evidence into standardized, executable causal queries, enhancing clinical AI transparency and verifiability.

Findings

Achieved 98.5% canonicalization accuracy.

Compiled 1,445 effect kernels from 754 studies.

80.5% query executability in real-world validation.

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.