Deep Learning Methods for Proximal Inference via Maximum Moment Restriction. (arXiv:2205.09824v1 [stat.ML])

The No Unmeasured Confounding Assumption is widely used to identify causal
effects in observational studies. Recent work on proximal inference has
provided alternative identification results that succeed even in the presence
of unobserved confounders, provided that one has measured a sufficiently rich
set of proxy variables, satisfying specific structural conditions. However,
proximal inference requires solving an ill-posed integral equation. Previous
approaches have used a variety of machine learning techniques to estimate a
solution to this integral equation, commonly referred to …

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