Abstract Number:
2663
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Speed
Participants:
Andy Shen (1), Samuel Pimentel (2)
Institutions:
(1) N/A, N/A, (2) University of California-Berkeley, N/A
Co-Author:
First Author:
Presenting Author:
Abstract Text:
Disparities in health or well-being experienced by racial and sexual minority groups can be difficult to study using the traditional exposure-outcome paradigm in causal inference, since potential outcomes in variables such as race or sexual minority status are challenging to interpret. Decomposition analysis addresses this gap by considering causal impacts on a disparity via interventions to other, intervenable exposures that may play a mediating role in the disparity. Moreover, decomposition analyses are conducted in observational settings and require untestable assumptions that rule out unmeasured confounders. Using the marginal sensitivity model, we develop a sensitivity analysis for unobserved confounders in studies of disparities. We use the percentile bootstrap to construct valid confidence intervals for disparities and causal effects on disparities under given levels of confounding under mild conditions. We also explore amplifications that give insight into multiple confounding mechanisms. We illustrate our framework on a study examining disparities in youth suicide rates among sexual minorities using the Adolescent Brain Cognitive Development Study.
Keywords:
Causal Inference|Sensitivity Analysis|Causal Decompositions|Disparity|Weighting|
Sponsors:
Section on Statistics in Epidemiology
Tracks:
Causal Inference
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