Year: 2026
Type: preprint
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.
Source: arXiv (Cornell University)
Institutions: AgroParisTech, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Mathématiques et Informatique Appliquées, Servier (France), Inserm, UniCancer Group, Université de Versailles Saint-Quentin-en-Yvelines, Université Paris-Saclay, Centre de recherche en Epidémiologie et Santé des Populations, Université Sorbonne Paris Nord, Centre de Recherche Épidémiologie et Statistique, Université Paris Cité
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7221155210