自动化因果引擎，提升真实世界研究可信度。
TrialCalibre: A Fully Automated Causal Engine for RCT Benchmarking and Observational Trial Calibration
- 多智能体协作自动完成实验模拟与校准流程。
- 通过对比随机对照试验，校准新适应症的因果效应。
- 适合需要高效、可审计真实世界证据的研究者。
真实世界证据（RWE）研究通过模拟目标试验日益影响监管与临床决策，但残留的、难以量化的偏倚仍限制其可信度。近期提出的BenchExCal框架通过‘基准、扩展、校准’两阶段流程应对此问题：首先将观察性模拟与现有随机对照试验（RCT）对比，再利用观测到的差异校准第二个模拟以估计新适应症的因果效应。尽管方法强大，BenchExCal资源消耗大且难于扩展。本文提出TrialCalibre，一个概念化的多智能体系统，旨在自动化并规模化BenchExCal工作流。该框架包含协调器、方案设计、数据合成、临床验证和量化校准等专用智能体，协同完成全流程。TrialCalibre引入智能体学习（如RLHF）与知识共享板，支持自适应、可审计、透明的因果效应估计。
原文摘要 · Abstract (English)
Real-world evidence (RWE) studies that emulate target trials increasingly inform regulatory and clinical decisions, yet residual, hard-to-quantify biases still limit their credibility. The recently proposed BenchExCal framework addresses this challenge via a two-stage Benchmark, Expand, Calibrate process, which first compares an observational emulation against an existing randomized controlled trial (RCT), then uses observed divergence to calibrate a second emulation for a new indication causal effect estimation. While methodologically powerful, BenchExCal is resource intensive and difficult to scale. We introduce TrialCalibre, a conceptualized multiagent system designed to automate and scale the BenchExCal workflow. Our framework features specialized agents such as the Orchestrator, Protocol Design, Data Synthesis, Clinical Validation, and Quantitative Calibration Agents that coordi-nate the the overall process. TrialCalibre incorpo-rates agent learning (e.g., RLHF) and knowledge blackboards to support adaptive, auditable, and transparent causal effect estimation.
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