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Causal inference accounting for unobserved confounding after outcome regression and doubly robust estimation
Umeå University, Faculty of Social Sciences, Centre for Demographic and Ageing Research (CEDAR). (Stat4Reg)ORCID iD: 0000-0002-9107-6486
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics. (Stat4Reg)ORCID iD: 0000-0003-3187-1987
2018 (English)In: Biometrics, ISSN 0006-341X, E-ISSN 1541-0420Article in journal (Refereed) Accepted
Abstract [en]

Causal inference with observational data can be performed under an assumption of no unobserved confounders (unconfoundedness assumption). There is, however, seldom clear subject-matter or empirical evidence for such an assumption. We therefore develop uncertainty intervals for average causal effects based on outcome regression estimators and doubly robust estimators, which provide inference taking into account both sampling variability and uncertainty due to unobserved confounders. In contrast with sampling variation, uncertainty due to unobserved confounding does not decrease with increasing sample size. The intervals introduced are obtained by modeling the treatment assignment mechanism and its correlation with the outcome given the observed confounders, allowing us to derive the bias of the estimators due to unobserved confounders. We are thus also able to contrast the size of the bias due to violation of the unconfoundedness assumption, with bias due to misspecification of the models used to explain potential outcomes. This is illustrated through numerical experiments where bias due to moderate unobserved confounding dominates misspecification bias for typical situations in terms of sample size and modeling assumptions. We also study the empirical coverage of the uncertainty intervals introduced and apply the results to a study of the effect of regular food intake on health. An R-package implementing the inference proposed is available.

Place, publisher, year, edition, pages
2018.
Keywords [en]
Average causal effects, double robust, ignorability assumption, regular food intake, sensitivity analysis, uncertainty intervals
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:umu:diva-153868DOI: 10.1111/biom.13001OAI: oai:DiVA.org:umu-153868DiVA, id: diva2:1268523
Funder
Forte, Swedish Research Council for Health, Working Life and Welfare, 2013-2506Marianne and Marcus Wallenberg FoundationAvailable from: 2018-12-06 Created: 2018-12-06 Last updated: 2018-12-06

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Genbäck, Minnade Luna, Xavier

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