TrialCalibre: A Fully Automated Causal Engine for RCT Benchmarking and Observational Trial Calibration

Amir Habibdoust Lafmajani, Xing Song

Keywords: Causal Inference, Multi-Agent AI System, Real-World Data (RWD), RCT Benchmarking, Large Language Models (LLMs)

TL;DR: We introduce TrialCalibre, an Multi-Agent AI causal engine that automates RCT benchmarking and extends trial evidence to new causal questions through calibrated observational emulations.

Abstract:

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 multi-agent 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 coordinate the the overall process. TrialCalibre incorporates agent learning (e.g., RLHF) and knowledge blackboards to support adaptive, auditable, and transparent causal effect estimation.

Submission Number: 19