PreprintCC BY-NC-ND 4.0HAL
Formalization and consistency of clone-censor-weight
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.
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BibTeX
@misc{meah2026formalization,
title = {{Formalization and consistency of clone-censor-weight}},
author = {Iqraa Meah and Charlotte Voinot and Stefan Michiels and Noémie Simon-Tillaux and Claire Jouffroy-Zeller and Ines Vaz-Luis and Clément Berenfeld and François Petit},
howpublished = {HAL},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.12377},
eprint = {2610.12377},
archivePrefix = {arXiv},
url = {https://inria.hal.science/hal-05789392v1},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Formalization and consistency of clone-censor-weight
AU - Iqraa Meah
AU - Charlotte Voinot
AU - Stefan Michiels
AU - Noémie Simon-Tillaux
AU - Claire Jouffroy-Zeller
AU - Ines Vaz-Luis
AU - Clément Berenfeld
AU - François Petit
PY - 2026
DA - 2026/10/08
PB - HAL
DO - 10.48550/arxiv.2610.12377
UR - https://inria.hal.science/hal-05789392v1
AB - 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.
ER -