Distributionally enhanced marginal sensitivity model and bounds

Fuente: arXiv
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Main Authors: Zhang, Yi, Xu, Wenfu, Tan, Zhiqiang
Format: Preprint
Published: 2025
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author Zhang, Yi
Xu, Wenfu
Tan, Zhiqiang
author_facet Zhang, Yi
Xu, Wenfu
Tan, Zhiqiang
contents For sensitivity analysis against unmeasured confounding, we build on the marginal sensitivity model (MSM) and propose a new model, deMSM, by incorporating a second constraint on the shift of potential outcome distributions caused by unmeasured confounders in addition to the constraint on the shift of treatment probabilities. We show that deMSM leads to interpretable sharp bounds of common causal parameters and tightens the corresponding MSM bounds. Moreover, the sharp bounds are symmetric in the two deMSM constraints, which facilitates practical applications. Lastly, we compare deMSM with other MSM-related models in both model constraints and sharp bounds, and reveal new interpretations for later models.
format Preprint
id arxiv_https___arxiv_org_abs_2505_13868
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Distributionally enhanced marginal sensitivity model and bounds
Zhang, Yi
Xu, Wenfu
Tan, Zhiqiang
Methodology
For sensitivity analysis against unmeasured confounding, we build on the marginal sensitivity model (MSM) and propose a new model, deMSM, by incorporating a second constraint on the shift of potential outcome distributions caused by unmeasured confounders in addition to the constraint on the shift of treatment probabilities. We show that deMSM leads to interpretable sharp bounds of common causal parameters and tightens the corresponding MSM bounds. Moreover, the sharp bounds are symmetric in the two deMSM constraints, which facilitates practical applications. Lastly, we compare deMSM with other MSM-related models in both model constraints and sharp bounds, and reveal new interpretations for later models.
title Distributionally enhanced marginal sensitivity model and bounds
topic Methodology
url https://arxiv.org/abs/2505.13868