Web: http://arxiv.org/abs/2202.01864

Sept. 23, 2022, 1:13 a.m. | Sorawit Saengkyongam, Leonard Henckel, Niklas Pfister, Jonas Peters

stat.ML updates on arXiv.org arxiv.org

Instrumental variable models allow us to identify a causal function between
covariates $X$ and a response $Y$, even in the presence of unobserved
confounding. Most of the existing estimators assume that the error term in the
response $Y$ and the hidden confounders are uncorrelated with the instruments
$Z$. This is often motivated by a graphical separation, an argument that also
justifies independence. Positing an independence restriction, however, leads to
strictly stronger identifiability results. We connect to the existing
literature in …

arxiv distribution identification independent

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