Causal generative models provide a principled framework for answering observational, interventional, and counterfactual queries from observational data. However, many deep causal models rely on highly expressive architectures with opaque mechanisms, limiting auditability in high-stakes domains. We propose KaCGM, a causal generative model for mixed-type tabular data where each structural equation is parameterized by a Kolmogorov--Arnold Network (KAN). This decomposition enables direct inspection of learned causal mechanisms, including symbolic approximations and visualization of parent--child relationships, while preserving query-agnostic generative semantics. We introduce a validation pipeline based on distributional matching and independence diagnostics of inferred exogenous variables, allowing assessment using observational data alone. Experiments on synthetic and semi-synthetic benchmarks show competitive performance against state-of-the-art methods. A real-world cardiovascular case study further demonstrates the extraction of simplified structural equations and interpretable causal effects. These results suggest that expressive causal generative modeling and functional transparency can be achieved jointly, supporting trustworthy deployment in tabular decision-making settings. Code: https://github.com/aalmodovares/kacgm

翻译：因果生成模型为从观测数据回答观测性、干预性和反事实查询提供了一个有原则的框架。然而，许多深度因果模型依赖于具有不透明机制的高度表达性架构，限制了高风险领域的可审计性。我们提出了KaCGM，一种针对混合类型表格数据的因果生成模型，其中每个结构方程由Kolmogorov–Arnold网络（KAN）参数化。这种分解使得能够直接检查学习到的因果机制，包括符号近似以及父-子关系的可视化，同时保留与查询无关的生成语义。我们引入了一个基于推断外生变量的分布匹配和独立性诊断的验证流程，从而仅使用观测数据即可进行评估。在合成和半合成基准上的实验表明，该方法与最先进方法相比具有竞争性能。一个真实世界的心血管案例研究进一步展示了简化结构方程的提取和可解释的因果效应。这些结果表明，表达性因果生成建模与功能透明性可以同时实现，从而支持表格决策场景中的可信部署。代码：https://github.com/aalmodovares/kacgm