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

TL;DR

TrialCalibre is an automated multiagent system that scales and streamlines the BenchExCal framework for calibrating observational studies against RCTs, enhancing credibility in real-world evidence analysis.

Contribution

It introduces TrialCalibre, a multiagent system that automates and scales the BenchExCal workflow for causal effect estimation in real-world evidence studies.

Findings

Automates the BenchExCal process using specialized agents.

Supports adaptive, auditable, and transparent causal effect estimation.

Incorporates agent learning and knowledge blackboards for improved performance.

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 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.