Simple yet Sharp Sensitivity Analysis for Any Contrast Under Unmeasured Confounding
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911914271965184 |
|---|---|
| author | Peña, Jose M. |
| author_facet | Peña, Jose M. |
| contents | We extend our previous work on sensitivity analysis for the risk ratio and difference contrasts under unmeasured confounding to any contrast. We prove that the bounds produced are still arbitrarily sharp, i.e. practically attainable. We illustrate the usability of the bounds with real data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_07940 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Simple yet Sharp Sensitivity Analysis for Any Contrast Under Unmeasured Confounding Peña, Jose M. Methodology Machine Learning We extend our previous work on sensitivity analysis for the risk ratio and difference contrasts under unmeasured confounding to any contrast. We prove that the bounds produced are still arbitrarily sharp, i.e. practically attainable. We illustrate the usability of the bounds with real data. |
| title | Simple yet Sharp Sensitivity Analysis for Any Contrast Under Unmeasured Confounding |
| topic | Methodology Machine Learning |
| url | https://arxiv.org/abs/2406.07940 |