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Identifying Causal Effects With Proxy Variables of an Unmeasured Confounder.

ABSTRACT: We consider a causal effect that is confounded by an unobserved variable, but with observed proxy variables of the confounder. We show that, with at least two independent proxy variables satisfying a certain rank condition, the causal effect is nonparametrically identified, even if the measurement error mechanism, i.e., the conditional distribution of the proxies given the confounder, may not be identified. Our result generalizes the identification strategy of Kuroki & Pearl (2014) that rests on identification of the measurement error mechanism. When only one proxy for the confounder is available, or the required rank condition is not met, we develop a strategy to test the null hypothesis of no causal effect.

SUBMITTER: Miao W

PROVIDER: S-EPMC7746017 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Identifying Causal Effects With Proxy Variables of an Unmeasured Confounder.

Miao Wang W Geng Zhi Z Tchetgen Tchetgen Eric E

Biometrika 20180813 4

We consider a causal effect that is confounded by an unobserved variable, but with observed proxy variables of the confounder. We show that, with at least two independent proxy variables satisfying a certain rank condition, the causal effect is nonparametrically identified, even if the measurement error mechanism, i.e., the conditional distribution of the proxies given the confounder, may not be identified. Our result generalizes the identification strategy of Kuroki & Pearl (2014) that rests on ...[more]

PMID: 33343006

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