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Automating the Selection of Proxy Variables of Unmeasured Confounders
In this paper, we investigate the estimation of causal effects among multiple treatments and a single outcome, all of which are affected by unmeasured confounders, within a linear causal model, without prior knowledge of the validity of proxy variables.
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