Kernel Methods for Unobserved Confounding: Negative Controls, Proxies, and Instruments. (arXiv:2012.10315v3 [stat.ML] UPDATED)

Negative control is a strategy for learning the causal relationship between
treatment and outcome in the presence of unmeasured confounding. The treatment
effect can nonetheless be identified if two auxiliary variables are available:
a negative control treatment (which has no effect on the actual outcome), and a
negative control outcome (which is not affected by the actual treatment). These
auxiliary variables can also be viewed as proxies for a traditional set of
control variables, and they bear resemblance to instrumental …

arxiv kernel negative proxies