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Assessing causal effects in the presence of unmeasured confounding is achallenging problem. It has been previously shown that the causal effect isidentifiable when noise variables of the treatment and outcome are bothnon-Gaussian under linear model setting with hidden variables. In this paper,we investigate the problem of identifying the causal effect using the auxiliarycovariate and non-Gaussianity from the treatment. Our key idea is tocharacterize the impact of unmeasured confounders using an observed covariate,assuming they are all Gaussian. We demonstrate that the causal effect can beidentified using a measured covariate, and then extend the identificationresults to the multi-treatment setting. We further develop a simple estimationprocedure for calculating causal effects.

Identifiability of causal effects with non-Gaussianity and auxiliary covariates