用柯尔莫哥洛夫网络提升多处理效应估计精度
KANITE: Kolmogorov-Arnold Networks for ITE estimation
- 基于柯尔莫哥洛夫网络的非线性激活学习，替代传统MLP线性权重
- 在多个基准数据集上，PEHE和ATE误差均优于现有方法
- 适合需要高精度因果推断的医疗、政策评估等场景
我们提出KANITE框架，利用柯尔莫哥洛夫网络（KANs）在多重处理设定下进行个体处理效应（ITE）估计。与多层感知机（MLPs）学习线性权重不同，KAN通过学习单变量激活函数来提升估计精度。该框架包含两个核心结构：1）积分概率度量（IPM）架构，采用特定方式使用IPM损失以有效对齐多处理下的ITE估计；2）熵平衡（EB）架构，通过优化熵并满足协变量在各处理组间的平衡性来学习样本权重。在多个基准数据集上的大量实验表明，KANITE在ε_{PEHE}和ε_{ATE}指标上均优于当前最先进算法。实验凸显了KANITE在获得更优因果估计方面的优势，强调了KAN在多样化应用场景中推动因果推断方法发展的潜力。
原文摘要 · Abstract (English)
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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