mlsanityData to 14 Oct 2026

Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects Estimation

arXiv 2305.15984 ↗First posted 25 May 2023cs.LG stat.MEcited by 11

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Abstract (arXiv)

Estimation of individualized treatment effects (ITE) from observational studies is a fundamental problem in causal inference and holds significant importance across domains, including healthcare. However, limited observational datasets pose challenges in reliable ITE estimation as data have to be split among treatment groups to train an ITE learner. While information sharing among treatment groups can partially alleviate the problem, there is currently no general framework for end-to-end information sharing in ITE estimation. To tackle this problem, we propose a deep learning framework based on `textitsoft weight sharing' to train ITE learners, enabling textitdynamic end-to-end information sharing among treatment groups. The proposed framework complements existing ITE learners, and introduces a new class of ITE learners, referred to as textitHyperITE. We extend state-of-the-art ITE learners with textitHyperITE versions and evaluate them on IHDP, ACIC-2016, and Twins benchmarks. Our experimental results show that the proposed framework improves ITE estimation error, with increasing effectiveness for smaller datasets.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 38 of its 79 references that are in the map. First posted on arXiv 25 May 2023.

Builds on

38 of 79 references.

References · 38

  1. 2018Spectral Normalization for Generative Adversarial Networkscited by 1,636
  2. 2016HyperNetworkscited by 1,027
  3. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 600
  4. 2017Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learningcited by 478
  5. 2021Conflict-Averse Gradient Descent for Multi-task Learningcited by 459
  6. 2017Quasi-Oracle Estimation of Heterogeneous Treatment Effectscited by 332
  7. 2016Learning Representations for Counterfactual Inferencecited by 329
  8. 2019Adapting Neural Networks for the Estimation of Treatment Effectscited by 287
  9. 2019Continual learning with hypernetworkscited by 273
  10. 2015Recursive Partitioning for Heterogeneous Causal Effectscited by 255
  11. 2019MetaPruning: Meta Learning for Automatic Neural Network Channel Pruningcited by 227
  12. 2020A Survey on Causal Inferencecited by 202
26 more
  1. 2015The Benefit of Multitask Representation Learningcited by 195
  2. 2021Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithmscited by 144
  3. 2017Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competitioncited by 106
  4. 2021On Inductive Biases for Heterogeneous Treatment Effect Estimationcited by 75
  5. 2018Stochastic Hyperparameter Optimization through Hypernetworkscited by 73
  6. 2020Treatment effect estimation with disentangled latent factorscited by 63
  7. 2020Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Searchcited by 38
  8. 2023In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimationcited by 29
  9. 2022Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimationcited by 21
  10. 2022Federated Learning with Heterogeneous Architectures using Graph HyperNetworkscited by 19
  11. 2023HyperINR: A Fast and Predictive Hypernetwork for Implicit Neural Representations via Knowledge Distillationcited by 19
  12. 2023Zero-shot causal learningcited by 17
  13. 2022Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretabilitycited by 15
  14. 2018Transfer Learning for Estimating Causal Effects using Neural Networkscited by 13
  15. 2023A Framework for Controllable Pareto Front Learning with Completed Scalarization Functions and its Applicationscited by 13
  16. 2019A Generative Model for Sampling High-Performance and Diverse Weights for Neural Networkscited by 12
  17. 2017HyperNetworks with statistical filtering for defending adversarial examplescited by 9
  18. 2019Predictive Uncertainty Quantification with Compound Density Networkscited by 9
  19. 2022To Impute or not to Impute? Missing Data in Treatment Effect Estimationcited by 9
  20. 2022Adversarial De-confounding in Individualised Treatment Effects Estimationcited by 9
  21. 2023Quantum HyperNetworks: Training Binary Neural Networks in Quantum Superpositioncited by 8
  22. 2021HCR-Net: A deep learning based script independent handwritten character recognition networkcited by 7
  23. 2022HyperNST: Hyper-Networks for Neural Style Transfercited by 7
  24. 2023The Challenges of Hyperparameter Tuning for Accurate Causal Effect Estimationcited by 6
  25. 2022Transfer Learning for Individual Treatment Effect Estimationcited by 5
  26. 2023Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Datacited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 4; 2025: 5; 2026: 1.

11 papers.

  1. 2023A Brief Review of Hypernetworks in Deep Learningcited by 102
  2. 2024HyperMARL: Adaptive Hypernetworks for Multi-Agent RLcited by 11
  3. 2025In-context Learning of Evolving Data Streams with Tabular Foundational Modelscited by 8
  4. 2024Progressive Generalization Risk Reduction for Data-Efficient Causal Effect Estimationcited by 3
  5. 2024Federated Learning for Estimating Heterogeneous Treatment Effectscited by 2
  6. 2025Exploiting Task Relationships in Continual Learning via Transferability-Aware Task Embeddingscited by 2
  7. 2025Individualised Treatment Effects Estimation with Composite Treatments and Composite Outcomescited by 1
  8. 2024Sample Selection Bias in Machine Learning for Healthcarecited by 0
  9. 2025Reliable Selection of Heterogeneous Treatment Effect Estimatorscited by 0
  10. 2025Advantages and limitations in the use of transfer learning for individual treatment effects in causal machine learningcited by 0
  11. 2026Big2Small: A Unifying Neural Network Framework for Model Compressioncited by 0