Improving Treatment Effect Estimation in Trials through Adaptive Borrowing of External Controls

2026-04-15

AI Summary

This study addresses the low efficiency of treatment effect estimation in small-sample randomized controlled trials and the bias introduced by naively incorporating external control data due to distributional discrepancies. To overcome these challenges, the authors propose an influence function–based adaptive sample borrowing framework that dynamically selects an optimal subset of external controls by quantifying their comparability to the target population, thereby minimizing the mean squared error of the average treatment effect estimator. The approach integrates outcome calibration to enhance data utilization efficiency and automatically excludes unreliable external samples under mild distributional assumptions, ensuring both robustness and high precision. Empirical evaluations on both simulated and real-world datasets demonstrate that the proposed method significantly outperforms existing strategies, yielding more accurate and efficient treatment effect estimates.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: State Estimation

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Randomized controlled trials (RCTs) often suffer from limited inferential efficiency in estimating treatment effects due to their small sample sizes. In recent years, incorporating external controls (ECs) has gained increasing attention as an effective way to augment small RCTs and thereby enhance estimation efficiency. However, ECs are not always comparable to RCTs, and direct borrowing without careful evaluation can introduce substantial bias and, paradoxically, undermine the accuracy of treatment effect estimation. In this paper, we propose a novel adaptive influence-based sample borrowing framework to improve average treatment effect (ATE) estimation in RCTs. The framework quantifies the ``comparability'' of each sample in ECs using influence functions and identifies the optimal subset of ECs that minimizes the mean squared error of the ATE estimator. The proposed framework is assumption-lean regarding the distribution of ECs and is robust to outliers, making it broadly applicable across diverse settings. Moreover, we develop an outcome calibration method to improve the data utilization efficiency of ECs, further strengthening the adaptive influence-based sample-borrowing framework. We demonstrate the effectiveness of the proposed method using both simulated and real-world datasets.

Problem

• treatment effect estimation
• external controls
• randomized controlled trials
• bias
• comparability

Innovation

• adaptive borrowing
• influence function
• external controls
• treatment effect estimation
• outcome calibration