Bias formulas for violations of proximal identification assumptions in a linear structural equation model
Name
10.1515_jci-2023-0039.pdf
Description
Published version
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4.73 MB
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Author(s)
Cobzaru, Raluca
Welsch, Roy
Finkelstein, Stan
Ng, Kenney
Shahn, Zach
Date Issued
June 19, 2024
Journal
Journal of Causal Inference
Publisher
Walter de Gruyter GmbH
Citation
Cobzaru, Raluca, Welsch, Roy, Finkelstein, Stan, Ng, Kenney and Shahn, Zach. "Bias formulas for violations of proximal identification assumptions in a linear structural equation model" Journal of Causal Inference, vol. 12, no. 1, 2024, pp. 20230039.
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Operations Research Center
MIT-IBM Watson AI Lab
MIT Institute for Data, Systems, and Society
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1515/jci-2023-0039
