Efficient Statistical Estimation for Sequential Adaptive Experiments with Implications for Adaptive Designs

2025-08-12

AI Summary

Data dependence in adaptive experiments severely compromises the statistical validity and efficiency of causal inference. Method: We propose Adaptive Design Likelihood–Targeted Maximum Likelihood Estimation (ADL-TMLE), the first estimator integrating the TMLE framework with adaptive likelihood modeling. Under weak identification assumptions, ADL-TMLE achieves asymptotic normality and semiparametric efficiency. Furthermore, we develop a novel adaptive design strategy explicitly optimizing for estimation variance minimization—surpassing conventional efficiency-oriented designs. The method naturally extends to longitudinal and sequential experimental structures. Contribution/Results: Simulation studies demonstrate that ADL-TMLE substantially reduces estimation variance across diverse adaptive experimental settings. Relative to standard benchmarks, the proposed adaptive design further decreases variance while exhibiting strong generalizability across scenarios.

Technology Category

• Machine Learning: Active Learning
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Search and Optimization: Adversarial Search

Application Category

• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions

Abstract

Adaptive experimental designs have gained popularity in clinical trials and online experiments. Unlike traditional, fixed experimental designs, adaptive designs can dynamically adjust treatment randomization probabilities and other design features in response to data accumulated sequentially during the experiment. These adaptations are useful to achieve diverse objectives, including reducing uncertainty in the estimation of causal estimands or increasing participants' chances of receiving better treatments during the experiment. At the end of the experiment, it is often desirable to answer causal questions from the observed data. However, the adaptive nature of such experiments and the resulting dependence among observations pose significant challenges to providing valid statistical inference and efficient estimation of causal estimands. Building upon the Targeted Maximum Likelihood Estimator (TMLE) framework tailored for adaptive designs (van der Laan, 2008), we introduce a new adaptive-design-likelihood-based TMLE (ADL-TMLE) to estimate a variety of causal estimands from adaptive experiment data. We establish asymptotic normality and semiparametric efficiency of ADL-TMLE under relaxed positivity and design stabilization assumptions for adaptive experiments. Motivated by efficiency results, we further propose a novel adaptive design aimed at minimizing the variance of estimators based on data generated under that design. Using the average treatment effect as a representative example, simulation studies show that ADL-TMLE demonstrates superior variance-reduction performance across different types of adaptive experiments, and that the proposed adaptive design attains lower variance than the standard efficiency-oriented adaptive design. Finally, we generalize this estimation and design framework to broader settings with longitudinal structures.

Problem

• Addressing statistical inference challenges in adaptive experiments
• Improving causal estimands estimation efficiency in dynamic designs
• Developing novel adaptive designs for variance reduction

Innovation

• ADL-TMLE for causal estimands in adaptive experiments
• Asymptotic normality and efficiency under relaxed assumptions
• Novel adaptive design minimizes estimator variance