SLOACI: Surrogate-Leveraged Online Adaptive Causal Inference

TL;DR

SLOACI introduces a novel framework that leverages surrogate outcomes in online adaptive experiments to improve efficiency and robustness, providing theoretical guarantees and practical tools for sequential testing.

Contribution

It is the first to integrate predictive surrogates into adaptive causal inference with theoretical efficiency guarantees and practical sequential testing tools.

Findings

Achieves semiparametric efficiency bound asymptotically.

Provides non-asymptotic regret bounds for practical performance.

Demonstrates superior finite-sample performance in simulations.

Abstract

Adaptive experimental designs have gained increasing attention across a range of domains. In this paper, we propose a new methodological framework, surrogate-leveraged online adaptive causal inference (SLOACI), which integrates predictive surrogate outcomes into adaptive designs to enhance efficiency. For downstream analysis, we construct the adaptive augmented inverse probability weighting estimator for the average treatment effect using collected data. Our procedure remains robust even when surrogates are noisy or weak. We provide a comprehensive theoretical foundation for SLOACI. Under the asymptotic regime, we show that the proposed estimator attains the semiparametric efficiency bound. From a non-asymptotic perspective, we derive a regret bound to provide practical insights. We also develop a toolbox of sequential testing procedures that accommodates both asymptotic and non-asymptotic regimes, allowing experimenters to choose the perspective that best aligns with their practical needs. Extensive simulations and a synthetic case study are conducted to showcase the superior finite-sample performance of our method.