A1278
Title: Causal inference with proxy controls in high-dimensional linear models
Authors: Ben Deaner - UCL (United Kingdom) [presenting]
Abstract: Recent literature considers causal inference using two vectors of noisy proxies for unobserved confounding factors. We consider linear models in which the vectors of proxies are potentially high-dimensional, and there may be many unobserved confounders. A key insight is that if each group of proxies is strictly larger than the number of confounding factors, this implies rank restrictions on matrices of nuisance parameters. We can exploit the rank-restriction to reduce the number of free parameters to be estimated. The number of unobserved confounders is not known a prior, but we show that it is identified, and we apply penalization methods to adapt to this quantity. We develop doubly-robust estimation and inference methods. We examine the asymptotic properties of these techniques and provide simulation evidence that they are effective.
