Journal
BIOMETRIKA
Volume 105, Issue 4, Pages 987-993Publisher
OXFORD UNIV PRESS
DOI: 10.1093/biomet/asy038
Keywords
Confounder; Identification; Measurement error; Negative control; Proxy
Funding
- China Scholarship Council
- U.S. National Institutes of Health
Ask authors/readers for more resources
We consider a causal effect that is confounded by an unobserved variable, but for which observed proxy variables of the confounder are available. We show that with at least two independent proxy variables satisfying a certain rank condition, the causal effect can be 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), which rests on identification of the measurement error mechanism. When only one proxy for the confounder is available, or when the required rank condition is not met, we develop a strategy for testing the null hypothesis of no causal effect.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available