Exploiting Independent Instruments: Identification and Distribution Generalization. (arXiv:2202.01864v2 [stat.ML] UPDATED)

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