In this paper, our goal is to generate synthetic data for heterogeneous (mixed-type) tabular datasets with high machine learning utility (MLu). Since the MLu performance depends on accurately approximating the conditional distributions, we focus on devising a synthetic data generation method based on conditional distribution estimation. We introduce MaCoDE by redefining the consecutive multi-class classification task of Masked Language Modeling (MLM) as histogram-based non-parametric conditional density estimation. Our approach enables the estimation of conditional densities across arbitrary combinations of target and conditional variables. We bridge the theoretical gap between distributional learning and MLM by demonstrating that minimizing the orderless multi-class classification loss leads to minimizing the total variation distance between conditional distributions. To validate our proposed model, we evaluate its performance in synthetic data generation across 10 real-world datasets, demonstrating its ability to adjust data privacy levels easily without re-training. Additionally, since masked input tokens in MLM are analogous to missing data, we further assess its effectiveness in handling training datasets with missing values, including multiple imputations of the missing entries.

Graph Neural Networks (GNNs) have significantly advanced in analyzing graph-structured data; however, their explainability remains challenging, affecting their applicability in critical domains such as medicine and pharmacology. In particular, violating the subgraph structure can degrade model interpretability and generalization performance. To address this problem, we propose a hierarchical and explainable causal inference-based GNN. Our model selects features based on explainable subgraph units informed by prior knowledge. Our method begins by clustering molecules into functional groups via the BRICS algorithm, then constructing a hierarchical structure at both the node and motif levels. The proposed model employs a gate module that distills causal features on the motif level and a loss function that disconnects information flow from non-causal features to the target level. The classification results on real-world molecular graphs demonstrate that our model outperforms other causal inference-based GNN models. In addition, it is confirmed that leveraging molecular docking data effectively identifies true causal substructures in the proposed model.