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 $\epsilon_{\text{PEHE}}$ and $\epsilon_{\text{ATE}}$ 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.

翻译：本文提出KANITE框架，该框架利用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。通过利用KAN学习单变量激活函数的独特能力（而非多层感知机（MLPs）所采用的线性权重学习方式），我们提升了ITE的估计精度。KANITE框架包含两个关键架构：1. 积分概率度量（IPM）架构：该架构以专门化的方式采用IPM损失函数，以有效对齐多重处理下的ITE估计；2. 熵平衡（EB）架构：该架构通过优化熵并约束不同处理组间协变量的平衡性，学习样本权重。在多个基准数据集上的广泛评估表明，KANITE在$\epsilon_{\text{PEHE}}$和$\epsilon_{\text{ATE}}$指标上均优于现有先进算法。实验凸显了KANITE在提升因果估计精度方面的优势，并强调了KAN在推动跨领域因果推断方法学发展方面的潜力。