Bias formulas for violations of proximal identification assumptions in a linear structural equation model
Abstract: Causal inference from observational data often rests on the unverifiable assumption of no unmeasured confounding. Recently, Tchetgen Tchetgen and colleagues have introduced proximal inference to leverage negative control outcomes and exposures as proxies to adjust for bias from unmeasured confounding. However, some of the key assumptions that proximal inference relies on are themselves empirically untestable. In addition, the impact of violations of proximal inference assumptions on the bias of effect estimates is not well understood. In this article, we derive bias formulas for proximal inference estimators under a linear structural equation model. These results are a first step toward sensitivity analysis and quantitative bias analysis of proximal inference estimators. While limited to a particular family of data generating processes, our results may offer some more general insight into the behavior of proximal inference estimators.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Bias formulas for violations of proximal identification assumptions in a linear structural equation model ; volume:12 ; number:1 ; year:2024 ; extent:34
Journal of causal inference ; 12, Heft 1 (2024) (gesamt 34)
- Creator
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Cobzaru, Raluca
Welsch, Roy
Finkelstein, Stan
Ng, Kenney
Shahn, Zach
- DOI
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10.1515/jci-2023-0039
- URN
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urn:nbn:de:101:1-2406201536351.707168948971
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 10:55 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Cobzaru, Raluca
- Welsch, Roy
- Finkelstein, Stan
- Ng, Kenney
- Shahn, Zach
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