KANITE: Kolmogorov-Arnold Networks for ITE estimation
This work addresses causal inference for estimating treatment effects, potentially benefiting fields like healthcare or policy, but appears incremental as it adapts an existing neural network architecture (KANs) to a specific causal task.
The paper tackles Individual Treatment Effect (ITE) estimation under multiple treatments in causal inference by introducing KANITE, a framework using Kolmogorov-Arnold Networks (KANs), and demonstrates that it outperforms state-of-the-art algorithms on benchmark datasets in both ε_PEHE and ε_ATE metrics.
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.