MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms
This addresses missing data issues for machine learning practitioners, offering a novel regularization approach that is incremental in enhancing existing imputation methods.
The paper tackles the problem of missing data in machine learning by developing MIRACLE, a causally-aware imputation algorithm that preserves the causal structure of the data, and it consistently improves imputation over benchmark methods across all missingness scenarios.
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.