Simple yet sharp sensitivity analysis for unmeasured confounding

Peña Jose M. ()
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Peña Jose M.: Department of Computer and Information Science, Linköping University, 581 83 Linköping, Sweden

Journal of Causal Inference, 2022, vol. 10, issue 1, 1-17

Abstract: We present a method for assessing the sensitivity of the true causal effect to unmeasured confounding. The method requires the analyst to set two intuitive parameters. Otherwise, the method is assumption free. The method returns an interval that contains the true causal effect and whose bounds are arbitrarily sharp, i.e., practically attainable. We show experimentally that our bounds can be tighter than those obtained by the method of Ding and VanderWeele, which, moreover, requires to set one more parameter than our method. Finally, we extend our method to bound the natural direct and indirect effects when there are measured mediators and unmeasured exposure–outcome confounding.

Keywords: sensitivity analysis; confounding; bounds; observational data (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:causin:v:10:y:2022:i:1:p:1-17:n:1

DOI: 10.1515/jci-2021-0041

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