BMC Medical Research Methodology | |
Frameworks for estimating causal effects in observational settings: comparing confounder adjustment and instrumental variables | |
Review | |
Joshua D. Grill1  Roy S. Zawadzki2  Daniel L. Gillen2  | |
[1] Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, USA;Department of Neurobiology and Behavior, University of California, Irvine, Irvine, USA;Department of Statistics, University of California, Irvine, Irvine, USA; | |
关键词: Causal inference; Observational studies; Instrumental variables; Adjustment; Propensity scores; | |
DOI : 10.1186/s12874-023-01936-2 | |
received in 2022-10-24, accepted in 2023-04-25, 发布年份 2023 | |
来源: Springer | |
【 摘 要 】
To estimate causal effects, analysts performing observational studies in health settings utilize several strategies to mitigate bias due to confounding by indication. There are two broad classes of approaches for these purposes: use of confounders and instrumental variables (IVs). Because such approaches are largely characterized by untestable assumptions, analysts must operate under an indefinite paradigm that these methods will work imperfectly. In this tutorial, we formalize a set of general principles and heuristics for estimating causal effects in the two approaches when the assumptions are potentially violated. This crucially requires reframing the process of observational studies as hypothesizing potential scenarios where the estimates from one approach are less inconsistent than the other. While most of our discussion of methodology centers around the linear setting, we touch upon complexities in non-linear settings and flexible procedures such as target minimum loss-based estimation and double machine learning. To demonstrate the application of our principles, we investigate the use of donepezil off-label for mild cognitive impairment. We compare and contrast results from confounder and IV methods, traditional and flexible, within our analysis and to a similar observational study and clinical trial.
【 授权许可】
CC BY
© The Author(s) 2023
【 预 览 】
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