mlsanityData to 14 Oct 2026

KANITE: Kolmogorov-Arnold Networks for ITE estimation

arXiv 2503.13912 ↗First posted 18 Mar 2025cs.LG cs.AI stat.MEcited by 2

Not on the map. Why?

Abstract (arXiv)

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 εPEHE and εATE 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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 20 of its 36 references that are in the map. First posted on arXiv 18 Mar 2025.

Builds on

20 of 36 references.

References · 20

  1. 2021Convex optimizationcited by 2,515
  2. 2024KAN: Kolmogorov-Arnold Networkscited by 649
  3. 2016Learning Representations for Counterfactual Inferencecited by 329
  4. 2017Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competitioncited by 106
  5. 2024Convolutional Kolmogorov-Arnold Networkscited by 77
  6. 2024Kolmogorov Arnold Informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov Arnold Networkscited by 67
  7. 2018Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networkscited by 56
  8. 2019Learning Individual Causal Effects from Networked Observational Datacited by 56
  9. 2024Kolmogorov-Arnold Transformercited by 56
  10. 2024GKAN: Graph Kolmogorov-Arnold Networkscited by 53
  11. 2021Causal Effect Inference for Structured Treatmentscited by 39
  12. 2024Physics Informed Kolmogorov-Arnold Neural Networks for Dynamical Analysis via Efficent-KAN and WAV-KANcited by 25
8 more
  1. 2020GraphITE: Estimating Individual Effects of Graph-structured Treatmentscited by 18
  2. 2023Zero-shot causal learningcited by 17
  3. 2024F-KANs: Federated Kolmogorov-Arnold Networkscited by 14
  4. 2024Kolmogorov-Arnold Network for Online Reinforcement Learningcited by 9
  5. 2024Kolmogorov-Arnold Network Autoencoderscited by 8
  6. 2020Double Robust Representation Learning for Counterfactual Predictioncited by 5
  7. 2023Estimation of individual causal effects in network setup for multiple treatmentscited by 4
  8. 2024I See, Therefore I Do: Estimating Causal Effects for Image Treatmentscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025CausalKANs: interpretable treatment effect estimation with Kolmogorov-Arnold networkscited by 2
  2. 2026Kolmogorov-Arnold causal generative modelscited by 0