Constructing Evidence-Based Tailoring Variables for Adaptive Interventions

2025-06-03

AI Summary

This study addresses the empirical selection challenge of tailoring variables—such as assessment timing, decision points, and threshold criteria—in adaptive interventions (AIs). We propose the first empirically grounded framework for selecting tailoring variables specifically designed for causal inference and prescriptive decision-making. Diverging from conventional predictive modeling, our framework treats randomized experiments as the gold standard and systematically integrates optimization-focused randomized clinical trials (ORCTs), Sequential Multiple Assignment Randomized Trials (SMARTs), factorial designs, and hybrid designs, augmented by causal inference and decision science methodologies to directly evaluate the efficacy of intervention decision rules. We explicitly delineate the limitations of observational data in tailoring variable development and establish an experiment-based, reusable methodological pathway. This work provides both theoretical foundations and practical guidance for enhancing AI effectiveness while reducing implementation costs and participant burden.

Technology Category

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

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

Background: An adaptive intervention (ADI) uses individual information in order to select treatment, to improve effectiveness while reducing cost and burden. ADIs require tailoring variables: person- and potentially time-specific information used to decide whether and how to deliver treatment. Specifying a tailoring variable for an intervention requires specifying what to measure, when to measure it, when to make the resulting decisions, and what cutoffs should be used in making those decisions. This involves tradeoffs between specificity versus sensitivity, and between waiting for sufficient information versus intervening quickly. These questions are causal and prescriptive (what should be done and when), not merely predictive (what would happen if current conditions persist). Purpose: There is little specific guidance in the literature on how to empirically choose tailoring variables, including cutoffs, measurement times, and decision times. Methods: We review possible approaches for comparing potential tailoring variables and propose a framework for systematically developing tailoring variables. Results: Although secondary observational data can be used to select tailoring variables, additional assumptions are needed. A specifically designed randomized experiment for optimization purposes (an optimization randomized clinical trial or ORCT), in the form of a multi-arm randomized trial, sequential multiple assignment randomized trial, a factorial experiment, or hybrid among them, may provide a more direct way to answer these questions. Conclusions: Using randomization directly to inform tailoring variables would provide the most direct causal evidence, but designing a trial to compare both tailoring variables and treatments adds complexity; further methodological research is warranted.

Problem

• How to empirically choose tailoring variables for adaptive interventions
• Tradeoffs between specificity, sensitivity, and timing in tailoring variables
• Designing randomized trials to optimize tailoring variables and treatments

Innovation

• Uses individual data for adaptive treatment selection
• Proposes framework for systematic tailoring variables
• Recommends randomized trials for causal evidence