Corpus ID: 226975974

Semiparametric proximal causal inference

@article{Cui2020SemiparametricPC,
  title={Semiparametric proximal causal inference},
  author={Yifan Cui and Hongming Pu and Xu Shi and W. Miao and E. Tchetgen},
  journal={arXiv: Methodology},
  year={2020}
}

Yifan Cui, Hongming Pu, +2 authors E. Tchetgen

Published 2020

Computer Science, Mathematics

arXiv: Methodology

Skepticism about the assumption of no unmeasured confounding, also known as exchangeability, is often warranted in making causal inferences from observational data; because exchangeability hinges on an investigator's ability to accurately measure covariates that capture all potential sources of confounding. In practice, the most one can hope for is that covariate measurements are at best proxies of the true underlying confounding mechanism operating in a given observational study. In this paper… CONTINUE READING

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