Web: http://arxiv.org/abs/2209.07111

Sept. 16, 2022, 1:13 a.m. | Sourabh Balgi, Jose M. Peña, Adel Daoud

stat.ML updates on arXiv.org arxiv.org

We propose a new sensitivity analysis model that combines copulas and
normalizing flows for causal inference under unobserved confounding. We refer
to the new model as $\rho$-GNF ($\rho$-Graphical Normalizing Flow), where
$\rho{\in}[-1,+1]$ is a bounded sensitivity parameter representing the backdoor
non-causal association due to unobserved confounding modeled using the most
well studied and widely popular Gaussian copula. Specifically, $\rho$-GNF
enables us to estimate and analyse the frontdoor causal effect or average
causal effect (ACE) as a function of $\rho$. We …

analysis arxiv

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