Statistical-Neural Interaction Networks for Interpretable Mixed-Type Data Imputation

TL;DR

The paper introduces SNI, an interpretable framework combining statistical priors and neural attention for mixed-type data imputation, providing dependency diagnostics and trade-off control.

Contribution

It proposes the CPFA module that learns prior-strength coefficients, enabling interpretable imputation and feature dependency analysis in mixed-type data.

Findings

SNI performs competitively on continuous variables under MCAR/strict-MAR conditions.

Provides intrinsic feature dependency diagnostics without post-hoc explainers.

Discusses limitations for severely imbalanced categorical targets.

Abstract

Real-world tabular databases routinely combine continuous measurements and categorical records, yet missing entries are pervasive and can distort downstream analysis. We propose Statistical-Neural Interaction (SNI), an interpretable mixed-type imputation framework that couples correlation-derived statistical priors with neural feature attention through a Controllable-Prior Feature Attention (CPFA) module. CPFA learns head-wise prior-strength coefficients $\{\lambda_h\}$ that softly regularize attention toward the prior while allowing data-driven deviations when nonlinear patterns appear to be present in the data. Beyond imputation, SNI aggregates attention maps into a directed feature-dependency matrix that summarizes which variables the imputer relied on, without requiring post-hoc explainers. We evaluate SNI against six baselines (Mean/Mode, MICE, KNN, MissForest, GAIN, MIWAE) on six datasets spanning ICU monitoring, population surveys, socio-economic statistics, and engineering applications. Under MCAR/strict-MAR at 30\% missingness, SNI is generally competitive on continuous metrics but is often outperformed by accuracy-first baselines (MissForest, MIWAE) on categorical variables; in return, it provides intrinsic dependency diagnostics and explicit statistical-neural trade-off parameters. We additionally report MNAR stress tests (with a mask-aware variant) and discuss computational cost, limitations -- particularly for severely imbalanced categorical targets -- and deployment scenarios where interpretability may justify the trade-off.