SCImputation: Mitigating Feature Confounding From a Structural Causal Perspective for Data Imputation

Missing data remain a critical challenge in data analysis, often leading to biased estimates and reduced reliability. Many imputation methods, operating under the assumption that similar instances share similar feature values, overlook the pivotal role of the feature associated with the missing value. Consequently, they apply uniform strategies to all missing values within an instance, often resulting in suboptimal neighbor sets and less accurate predictions. In this paper, we adopt a structural causal perspective to examine data imputation, revealing that once the target feature guides neighbor selection, it acts as a confounder and biases subsequent missing-value estimations. Building on this insight, we propose Structural Causal Imputation (SCImputation), a strategy that leverages both instance-level and feature-level information to refine neighbor selection, and applies a back-door adjustment formula that reweights local estimates with the global distribution, thereby mitigating the confounding introduced by the target feature. We integrate SCImputation with KNNimpute and LLSimpute, and evaluate the resulting variants on five datasets (NACC, three NCBI microarrays, and the Turkiye Student Evaluation) with ablation studies. In comparative evaluations against 12 baselines, SCImputation achieves 3.0%-4.6% gains in accuracy and 0.009-0.059 reductions in RMSE over the established approaches, and demonstrates competitive performance against leading deep learning baselines across diverse missingness mechanisms. These results suggest that SCImputation provides a causally grounded strategy for data imputation in both biomedical and general applications.