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Estimating the causal effects of an intervention from high-dimensionalobservational data is difficult due to the presence of confounding. The task isoften complicated by the fact that we may have a systematic missingness in ourdata at test time. Our approach uses the information bottleneck to perform alow-dimensional compression of covariates by explicitly considering therelevance of information. Based on the sufficiently reduced covariate, wetransfer the relevant information to cases where data is missing at test time,allowing us to reliably and accurately estimate the effects of an intervention,even where data is incomplete. Our results on causal inference benchmarks and areal application for treating sepsis show that our method achieves state-of-theart performance, without sacrificing interpretability.

Cause-Effect Deep Information Bottleneck For Systematically Missing Covariates