On Proximal Causal Learning with Many Hidden Confounders
Methodology
2020-12-15 v1
Abstract
We generalize the proximal g-formula of Miao, Geng, and Tchetgen Tchetgen (2018) for causal inference under unobserved confounding using proxy variables. Specifically, we show that the formula holds true for all causal models in a certain equivalence class, and this class contains models in which the total number of levels for the set of unobserved confounders can be arbitrarily larger than the number of levels of each proxy variable. Although straightforward to obtain, the result can be significant for applications. Simulations corroborate our formal arguments.
Cite
@article{arxiv.2012.06725,
title = {On Proximal Causal Learning with Many Hidden Confounders},
author = {Nikos Vlassis and Phil Hebda and Stephan McBride and Athanasios Noulas},
journal= {arXiv preprint arXiv:2012.06725},
year = {2020}
}