用可解释的网络构建因果生成模型，让复杂决策更透明可信。
Kolmogorov-Arnold causal generative models
- 用柯尔莫哥洛夫-阿诺德网络分解因果方程，实现机制可查。
- 在合成与半合成数据上表现优于现有方法，真实心血管数据验证有效。
- 适合需要可解释性的医疗、金融等高风险决策场景。
因果生成模型为从观测数据中回答观察、干预和反事实问题提供了严谨框架。然而，许多深度因果模型依赖高度表达但机制模糊的架构，限制了在高风险领域中的可审计性。我们提出KaCGM，一种用于混合类型表格数据的因果生成模型，其中每个结构方程由柯尔莫哥洛夫-阿诺德网络（KAN）参数化。该分解使得学习到的因果机制可直接审查，包括符号近似与父-子关系可视化，同时保持无查询依赖的生成语义。我们引入基于分布匹配和推断外生变量独立性诊断的验证流程，仅需观测数据即可评估模型。在合成与半合成基准上的实验表明其性能媲美最先进方法。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与可解释因果效应的发现。结果表明，在表格决策场景中，高表达力的因果生成建模与函数透明性可兼得，支持可信部署。代码见：https://github.com/aalmodovares/kacgm
原文摘要 · Abstract (English)
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
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