CausalKANs: 通过Kolmogorov-Arnold网络实现可解释的治疗效应估计
CausalKANs: interpretable treatment effect estimation with Kolmogorov-Arnold networks
AI总结：
causalKANs通过Kolmogorov-Arnold网络实现可解释的治疗效应估计，提供高准确性和可解释性的平衡解决方案。
摘要：
深度神经网络在估计异质治疗效应方面取得了最先进的性能，但其不透明性限制了在医学、经济学和公共政策等敏感领域中的信任和应用。基于已建立且性能优异的因果神经架构，我们提出了causalKANs，一个将神经估计器转换为Kolmogorov-Arnold网络（KANs）的框架。通过引入剪枝和符号简化，causalKANs能够在保持预测准确性的同时，产生可解释的闭式公式。在基准数据集上的实验表明，causalKANs在CATE误差度量上与神经基线表现相当，而简单的KAN变体也实现了具有竞争力的性能，提供了有利的准确性-可解释性权衡。通过结合可靠性与分析可得性，causalKANs提供了受闭式表达式和可解释图表支持的可审计估计器，使在高风险环境中能够实现可信的个性化决策。我们将在https://github.com/aalmodovares/causalkans上发布代码以确保可重复性。
英文摘要：
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 .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
