Cause-Effect Deep Information Bottleneck For Systematically Missing Covariates
This addresses a critical issue in causal inference for fields like healthcare, where missing data can hinder reliable effect estimation, though it is incremental in applying the information bottleneck to this specific missingness scenario.
The paper tackles the problem of estimating causal effects from high-dimensional observational data with systematic missing covariates at test time, achieving state-of-the-art performance on benchmarks and a real sepsis treatment application without losing interpretability.
Estimating the causal effects of an intervention from high-dimensional observational data is difficult due to the presence of confounding. The task is often complicated by the fact that we may have a systematic missingness in our data at test time. Our approach uses the information bottleneck to perform a low-dimensional compression of covariates by explicitly considering the relevance of information. Based on the sufficiently reduced covariate, we transfer 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 a real application for treating sepsis show that our method achieves state-of-the art performance, without sacrificing interpretability.