CG-DIFF: CAUSAL-GRAPH-GUIDED DIFFUSION MODELS FOR TABULAR IMPUTATION UNDER SELF-MASKING MNAR

Missing-value imputation under Self-Masking Missing Not At Random (MNAR) is challenging because observed and missing values may follow different distributions, making it difficult to recover missing values from observed data alone. We propose a Causal-Graph-guided DIFFusion model (CG-DIFF) for tabular missing-value imputation under Self-Masking MNAR. CG-DIFF introduces an observation mask into the forward diffusion process to enable training on observed values only, and develops a Causal-Graph-guided Noise Estimator (CG-NE) to estimate diffusion noise from incomplete data. CG-NE combines Kolmogorov–Arnold network projections with graph-masked attention, using either parents plus self or the Markov blanket plus self as the graph support. At each reverse step, CG-DIFF replaces the values at observed positions with their forward-noised counterparts at the corresponding timestep, thereby preserving observed information throughout sampling. We establish sufficient conditions under which the population predictor based on the Markov blanket has lower missing-case squared-error risk than the predictor based only on the parent set, providing theoretical motivation for the Markov-blanket-plus-self design. We evaluate CG-DIFF against nine competing imputation methods on simulated datasets and a real-world dataset. Experimental results show that CG-DIFF with the Markov-blanket-plus-self graph support achieves better imputation performance than the competing methods in most settings.