New bounds for the Probability of Causation in Mediation Analysis
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An individual has been subjected to some exposure and has developed some outcome. Using data on similar individuals, we wish to evaluate, for this case, the probability that the outcome was in fact caused by the exposure. Even with the best possible experimental data on exposure and outcome, we typically can not identify this "probability of causation" exactly, but we can provide information in the form of bounds for it. Under appropriate assumptions, these bounds can be tightened if we can make other observations (e.g., on non-experimental cases), measure additional variables (e.g., covariates) or measure complete mediators. In this work we propose new bounds for the case that a third variable mediates partially the effect of the exposure on the outcome.
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Cited by 1 Pith paper
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Bounding Causes of Effects with Mediators
Derives general bounds on probability of causation (PC) with sequences of complete mediators, proving that extremal bounds are achieved with at most two steps and that PC can be identified as 0 but never as 1.
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