MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms

Trent Kyono · Yao Zhang · Alexis Bellot · Mihaela van der Schaar

Keywords: [ Deep Learning ] [ Machine Learning ]

[ Abstract ]
[ OpenReview
Thu 9 Dec 12:30 a.m. PST — 2 a.m. PST


Missing data is an important problem in machine learning practice. Starting from the premise that imputation methods should preserve the causal structure of the data, we develop a regularization scheme that encourages any baseline imputation method to be causally consistent with the underlying data generating mechanism. Our proposal is a causally-aware imputation algorithm (MIRACLE). MIRACLE iteratively refines the imputation of a baseline by simultaneously modeling the missingness generating mechanism, encouraging imputation to be consistent with the causal structure of the data. We conduct extensive experiments on synthetic and a variety of publicly available datasets to show that MIRACLE is able to consistently improve imputation over a variety of benchmark methods across all three missingness scenarios: at random, completely at random, and not at random.

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