Bounding causal effects with an unknown mixture of informative and non-informative missingness
In experimental and observational data settings, researchers often have limited knowledge of the reasons for missing outcomes. To address this uncertainty, we propose bounds on causal effects for missing outcomes, accommodating the scenario where missingness is an unobserved mixture of informative a...
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| Auteurs principaux: | , , , , |
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| Format: | Artigo |
| Langue: | Inglês |
| Publié: |
De Gruyter
2026-06-01
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| Collection: | Journal of Causal Inference |
| Sujets: | |
| Accès en ligne: | https://doi.org/10.1515/jci-2025-0028 |
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