mlsanityData to 7 Oct 2026

Individualised Treatment Effects Estimation with Composite Treatments and Composite Outcomes

arXiv 2502.08282 ↗First posted 12 Feb 2025cs.LG cs.AIcited by 1

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Abstract (arXiv)

Estimating individualised treatment effect (ITE) -- that is the causal effect of a set of variables (also called exposures, treatments, actions, policies, or interventions), referred to as textitcomposite treatments, on a set of outcome variables of interest, referred to as textitcomposite outcomes, for a unit from observational data -- remains a fundamental problem in causal inference with applications across disciplines, such as healthcare, economics, education, social science, marketing, and computer science. Previous work in causal machine learning for ITE estimation is limited to simple settings, like single treatments and single outcomes. This hinders their use in complex real-world scenarios; for example, consider studying the effect of different ICU interventions, such as beta-blockers and statins for a patient admitted for heart surgery, on different outcomes of interest such as atrial fibrillation and in-hospital mortality. The limited research into composite treatments and outcomes is primarily due to data scarcity for all treatments and outcomes. To address the above challenges, we propose a novel and innovative hypernetwork-based approach, called H-Learner, to solve ITE estimation under composite treatments and composite outcomes, which tackles the data scarcity issue by dynamically sharing information across treatments and outcomes. Our empirical analysis with binary and arbitrary composite treatments and outcomes demonstrates the effectiveness of the proposed approach compared to existing methods.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 29 of its 54 references that are in the map. First posted on arXiv 12 Feb 2025.

Builds on

29 of 54 references.

References · 29

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17 more
  1. 2024Estimating Epistemic and Aleatoric Uncertainty with a Single Modelcited by 52
  2. 2020Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Searchcited by 38
  3. 2023HyperINR: A Fast and Predictive Hypernetwork for Implicit Neural Representations via Knowledge Distillationcited by 19
  4. 2023A Framework for Controllable Pareto Front Learning with Completed Scalarization Functions and its Applicationscited by 13
  5. 2019A Generative Model for Sampling High-Performance and Diverse Weights for Neural Networkscited by 11
  6. 2023Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects Estimationcited by 11
  7. 2019Predictive Uncertainty Quantification with Compound Density Networkscited by 9
  8. 2022Adversarial De-confounding in Individualised Treatment Effects Estimationcited by 9
  9. 2024Effective Heterogeneous Federated Learning via Efficient Hypernetwork-based Weight Generationcited by 9
  10. 2017HyperNetworks with statistical filtering for defending adversarial examplescited by 8
  11. 2023Quantum HyperNetworks: Training Binary Neural Networks in Quantum Superpositioncited by 8
  12. 2024Hyper-CL: Conditioning Sentence Representations with Hypernetworkscited by 8
  13. 2021HCR-Net: A deep learning based script independent handwritten character recognition networkcited by 7
  14. 2025Orthogonal Representation Learning for Estimating Causal Quantitiescited by 4
  15. 2024Learning to Forget using Hypernetworkscited by 3
  16. 2025HypeRL: Hypernetwork-Based Reinforcement Learning for Control of Parametrized Dynamical Systemscited by 3
  17. 2023HyperCoil-Recon: A Hypernetwork-based Adaptive Coil Configuration Task Switching Network for MRI Reconstructioncited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Barycentric Fused Gromov-Wasserstein Balancing for Causal Inference under Multiple Treatmentscited by 0