DoobImp: Diffusion Imputation without Artificial Masking

Missing data are ubiquitous in real-world datasets, making reliable imputation essential for prediction and downstream analysis. Diffusion-based imputation methods typically learn conditional scores by artificially masking observed entries during training. This couples learning to a user-specified mask distribution, which may not match test-time missingness and can degrade imputation performance. We introduce DoobImp, a diffusion imputation framework that avoids artificial masking by decoupling test-time imputation from learning an unconditional diffusion backbone. Its two complementary components address conditioning and learning. DoobCond uses Doob's h-transform to characterize the ideal conditional reverse diffusion process and derives a practical approximation for conditioning on observed entries. DoobFit learns the unconditional backbone from incomplete training data by alternating between imputing missing entries with DoobCond and refitting the unconditional score. Across nine tabular datasets under three missingness mechanisms, DoobImp achieves the best overall performance over 17 baselines, attaining an average MAE rank of 1.19 and the highest aggregate categorical accuracy. Experiments on four time-series datasets further demonstrate robust performance under both pointwise and structured missingness. The resulting tabular imputations also support strong downstream classification performance. These results show that decoupling unconditional score learning from conditioning provides an effective and broadly applicable alternative to mask-based diffusion imputation.