KANITE：用于ITE估计的Kolmogorov-Arnold网络
KANITE: Kolmogorov-Arnold Networks for ITE estimation
AI总结：
KANITE利用Kolmogorov-Arnold网络替代MLP，通过IPM和熵平衡两种架构改进多重处理下的个体处理效应估计，在基准数据集上优于现有方法。
摘要：
我们提出了KANITE，一个利用Kolmogorov-Arnold网络（KANs）在因果推断的多重处理设置下进行个体处理效应（ITE）估计的框架。通过利用KAN学习单变量激活函数的独特能力，而非多层感知器（MLPs）学习线性权重的方式，我们改进了ITE的估计。KANITE框架包含两个关键架构：1. 积分概率度量（IPM）架构：该架构以专门方式使用IPM损失，以有效对齐跨多重处理的ITE估计。2. 熵平衡（EB）架构：该架构通过优化熵并平衡处理组间的协变量来学习样本权重。在基准数据集上的广泛评估表明，KANITE在ε_PEHE和ε_ATE两个指标上均优于最先进的算法。我们的实验突出了KANITE在实现改进的因果估计方面的优势，强调了KANs在推动跨不同应用领域的因果推断方法方面的潜力。
英文摘要：
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.
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