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Identification of the outcome distribution and sensitivity analysis under weak confounder–instrument interaction
Recently, Wang and Tchetgen Tchetgen (2018) showed that the global average treatment effect is identifiable even in the presence of unmeasured confounders so long as they do not modify the instrument’s additive effect on the treatment. We use a simple and direct method to show that this no-interacti...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9634714/ https://www.ncbi.nlm.nih.gov/pubmed/36337258 http://dx.doi.org/10.1016/j.spl.2022.109590 |
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author | Mao, Lu |
author_facet | Mao, Lu |
author_sort | Mao, Lu |
collection | PubMed |
description | Recently, Wang and Tchetgen Tchetgen (2018) showed that the global average treatment effect is identifiable even in the presence of unmeasured confounders so long as they do not modify the instrument’s additive effect on the treatment. We use a simple and direct method to show that this no-interaction assumption allows identification of the entire outcome distribution, which leads to multiply robust estimation procedures for nonlinear functionals like the quantile and Mann–Whitney treatment effects. Similarly, we can bound these causal estimands through the outcome distribution in sensitivity analysis against confounder–instrument interaction. |
format | Online Article Text |
id | pubmed-9634714 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
record_format | MEDLINE/PubMed |
spelling | pubmed-96347142022-11-04 Identification of the outcome distribution and sensitivity analysis under weak confounder–instrument interaction Mao, Lu Stat Probab Lett Article Recently, Wang and Tchetgen Tchetgen (2018) showed that the global average treatment effect is identifiable even in the presence of unmeasured confounders so long as they do not modify the instrument’s additive effect on the treatment. We use a simple and direct method to show that this no-interaction assumption allows identification of the entire outcome distribution, which leads to multiply robust estimation procedures for nonlinear functionals like the quantile and Mann–Whitney treatment effects. Similarly, we can bound these causal estimands through the outcome distribution in sensitivity analysis against confounder–instrument interaction. 2022-10 2022-06-23 /pmc/articles/PMC9634714/ /pubmed/36337258 http://dx.doi.org/10.1016/j.spl.2022.109590 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) ). |
spellingShingle | Article Mao, Lu Identification of the outcome distribution and sensitivity analysis under weak confounder–instrument interaction |
title | Identification of the outcome distribution and sensitivity analysis under weak confounder–instrument interaction |
title_full | Identification of the outcome distribution and sensitivity analysis under weak confounder–instrument interaction |
title_fullStr | Identification of the outcome distribution and sensitivity analysis under weak confounder–instrument interaction |
title_full_unstemmed | Identification of the outcome distribution and sensitivity analysis under weak confounder–instrument interaction |
title_short | Identification of the outcome distribution and sensitivity analysis under weak confounder–instrument interaction |
title_sort | identification of the outcome distribution and sensitivity analysis under weak confounder–instrument interaction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9634714/ https://www.ncbi.nlm.nih.gov/pubmed/36337258 http://dx.doi.org/10.1016/j.spl.2022.109590 |
work_keys_str_mv | AT maolu identificationoftheoutcomedistributionandsensitivityanalysisunderweakconfounderinstrumentinteraction |