KANITE: Kolmogorov-Arnold Networks for ITE estimation

研究问题: 在存在多个处理选项时，如何利用可学习的一元激活函数与处理组间协变量平衡，提高个体处理效应的估计表现？
方法与思路: 作者提出基于 Kolmogorov–Arnold 网络的 KANITE，分别构建使用积分概率度量损失和熵平衡权重的两类架构，并在基准数据集上评估。
材料陈述的贡献: 摘要称，KANITE 在基准评估的 ε_PEHE 与 ε_ATE 指标上优于所比较算法，展示该网络用于多处理因果效应估计的潜力。
阅读关注点: 应核对因果识别假设、多个处理间的重叠程度、损失与权重的构造，以及基准效应真值、调参预算和比较算法的公平性。
阅读边界: 这是预印本，同行评审及期刊接收未核实；摘要未说明识别条件、数据细节和数值结果，所述优势不能直接推广到其他应用。

GPT-6.1 Sol · 高 · 2026-10-03

原文摘要

We introduce KANITE, a framework leveraging Kolmogorov-Arnold Networks (KANs) for Individual Treatment Effect (ITE) estimation under multiple treatments setting in causal inference. By utilizing KAN's unique abilities to learn univariate activation functions as opposed to learning linear weights by Multi-Layer Perceptrons (MLPs), we improve the estimates of ITEs. The KANITE framework comprises two key architectures: 1.Integral Probability Metric (IPM) architecture: This employs an IPM loss in a specialized manner to effectively align towards ITE estimation across multiple treatments. 2. Entropy Balancing (EB) architecture: This uses weights for samples that are learned by optimizing entropy subject to balancing the covariates across treatment groups. Extensive evaluations on benchmark datasets demonstrate that KANITE outperforms state-of-the-art algorithms in both $ε_{\text{PEHE}}$ and $ε_{\text{ATE}}$ metrics. Our experiments highlight the advantages of KANITE in achieving improved causal estimates, emphasizing the potential of KANs to advance causal inference methodologies across diverse application areas.We introduce KANITE, a framework leveraging Kolmogorov-Arnold Networks (KANs) for Individual Treatment Effect (ITE) estimation under multiple treatments setting in causal inference. By utilizing KAN's unique abilities to learn univariate activation functions as opposed to learning linear weights by Multi-Layer Perceptrons (MLPs), we improve the estimates of ITEs. The KANITE framework comprises two key architectures: 1.Integral Probability Metric (IPM) architecture: This employs an IPM loss in a specialized manner to effectively align towards ITE estimation across multiple treatments. 2. Entropy Balancing (EB) architecture: This uses weights for samples that are learned by optimizing entropy subject to balancing the covariates across treatment groups. Extensive evaluations on benchmark datasets demonstrate that KANITE outperforms state-of-the-art algorithms in both $ε_{\text{PEHE}}$ and $ε_{\text{ATE}}$ metrics. Our experiments highlight the advantages of KANITE in achieving improved causal estimates, emphasizing the potential of KANs to advance causal inference methodologies across diverse application areas.

摘要来源：arXiv

笔记与分组

分类依据与纠错

KANITE: Kolmogorov-Arnold Networks for ITE estimation

作者的期刊发表依据

代码、数据与补充材料

KANITE：用于个体处理效应估计的 Kolmogorov–Arnold 网络

原文摘要

同方向的基础文献

同方向的基础文献