用可解释的数学公式替代黑箱模型，精准估算个体治疗效果
CausalKANs: interpretable treatment effect estimation with Kolmogorov-Arnold networks
- 将神经网络转换为基于柯尔莫哥洛夫-阿诺德定理的可解释结构
- 在基准数据集上保持与神经网络相当的预测准确率
- 生成闭式表达式和可视化图谱，适合医疗等高风险场景
深度神经网络在估计异质性治疗效应方面表现优异，但其不透明性限制了其在医学、经济和公共政策等敏感领域的信任与应用。本文提出因果KAN（causalKANs），将条件平均治疗效应（CATE）的神经估计算法转化为柯尔莫哥洛夫-阿诺德网络（KAN）。通过引入剪枝与符号简化，causalKANs 在保持预测精度的同时生成可解释的闭式表达式。在多个基准数据集上的实验表明，causalKANs 在 CATE 误差指标上与神经基线性能相当；即使简单的 KAN 变体也表现出竞争力，实现了良好的准确率-可解释性权衡。该框架通过闭式表达式与可读图示，提供可审计的估计器，支持高风险场景下的可信个性化决策。代码已开源：https://github.com/aalmodovares/causalkans。
原文摘要 · Abstract (English)
Deep neural networks achieve state-of-the-art performance in estimating heterogeneous treatment effects, but their opacity limits trust and adoption in sensitive domains such as medicine, economics, and public policy. Building on well-established and high-performing causal neural architectures, we propose causalKANs, a framework that transforms neural estimators of conditional average treatment effects (CATEs) into Kolmogorov--Arnold Networks (KANs). By incorporating pruning and symbolic simplification, causalKANs yields interpretable closed-form formulas while preserving predictive accuracy. Experiments on benchmark datasets demonstrate that causalKANs perform on par with neural baselines in CATE error metrics, and that even simple KAN variants achieve competitive performance, offering a favorable accuracy--interpretability trade-off. By combining reliability with analytic accessibility, causalKANs provide auditable estimators supported by closed-form expressions and interpretable plots, enabling trustworthy individualized decision-making in high-stakes settings. We release the code for reproducibility at https://github.com/aalmodovares/causalkans .
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