On estimands in target trial emulation

2026-01-06

AI Summary

Traditional target trial emulation often assumes a constant treatment effect and relies on estimators vulnerable to model misspecification, leading to ambiguous causal interpretation. This work proposes a model-agnostic strategy centered on estimator selection, shifting the analytical focus from model specification to the construction of robust estimators by explicitly defining time-varying causal effects in a well-specified target population. The approach integrates G-computation with inverse probability weighting, accommodates diverse study designs, and dispenses with the assumption of a constant effect. Evaluated through simulations and applied to real-world data on antimicrobial de-escalation therapy in intensive care, the method substantially enhances the interpretability and robustness of causal effect estimates.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

The target trial framework enables causal inference from longitudinal observational data by emulating randomized trials initiated at multiple time points. Precision is often improved by pooling information across trials, with standard models typically assuming - among other things - a time-constant treatment effect. However, this obscures interpretation when the true treatment effect varies, which we argue to be likely as a result of relying on noncollapsible estimands. To address these challenges, this paper introduces a model-free strategy for target trial analysis, centered around the choice of the estimand, rather than model specification. This ensures that treatment effects remain clearly interpretable for well-defined populations even under model misspecification. We propose estimands suitable for different study designs, and develop accompanying G-computation and inverse probability weighted estimators. Applications on simulations and real data on antimicrobial de-escalation in an intensive care unit setting demonstrate the greater clarity and reliability of the proposed methodology over traditional techniques.

Problem

• target trial emulation
• estimands
• treatment effect heterogeneity
• noncollapsible measures
• causal inference

Innovation

• target trial emulation
• estimands
• model-free
• causal inference
• noncollapsible