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  1. Simple yet sharp sensitivity analysis for unmeasured confounding.Jose M. Peña - 2022 - Journal of Causal Inference 10 (1):1-17.
    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 (...)
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  • Novel bounds for causal effects based on sensitivity parameters on the risk difference scale.Ola Hössjer & Arvid Sjölander - 2021 - Journal of Causal Inference 9 (1):190-210.
    Unmeasured confounding is an important threat to the validity of observational studies. A common way to deal with unmeasured confounding is to compute bounds for the causal effect of interest, that is, a range of values that is guaranteed to include the true effect, given the observed data. Recently, bounds have been proposed that are based on sensitivity parameters, which quantify the degree of unmeasured confounding on the risk ratio scale. These bounds can be used to compute an E-value, that (...)
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