Formalization and consistency of clone-censor-weight

2026-10-08

AI Summary

This study addresses the insufficient statistical interpretation of the Causal Chain Weighting (CCW) method in causal inference from observational data. Drawing upon admissible set theory and stochastic intervention frameworks, we rigorously formalize the CCW method from a statistical perspective. By explicitly defining its causal estimand, this work establishes, for the first time, a statistical theoretical foundation for CCW under complex treatment definitions. We prove the asymptotic consistency of the CCW estimator and provide rigorous theoretical guarantees for bootstrap-based inference. Ultimately, this research bridges a critical theoretical gap in the CCW methodology, laying a solid statistical foundation for its application in causal inference.

Abstract

Target trial emulation has become a standard framework for causal inference from observational data. Within this paradigm, the clone-censor-weight (CCW) methodology provides a practical way to deal with complex treatment and adherence definitions when treatment regimes are not distinguishable at baseline. Despite its increasing use, the statistical interpretation of CCW remains limited. In this work, we formalize the CCW methodology from a statistical viewpoint based on admissible sets and stochastic interventions. We characterize the causal estimands targeted by CCW, establish consistency of CCW estimators under standard identification assumptions, and provide theoretical guarantees for bootstrap-based inference methods.

Problem

• clone-censor-weight
• target trial emulation
• causal inference
• observational data
• consistency

Innovation

• clone-censor-weight
• target trial emulation
• stochastic interventions
• causal inference
• consistency