# Individualised Treatment Effects Estimation with Composite Treatments and Composite Outcomes

arXiv: https://arxiv.org/abs/2502.08282 · HTML: https://mlsanity.com/paper/2502.08282/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 54 references that are in the map.
- First posted on arXiv 12 Feb 2025.

## Builds on (most cited first, 29 of 29)

- [HyperNetworks](https://mlsanity.com/paper/1609.09106/) (2016)
- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://mlsanity.com/paper/1706.03461/) (2017)
- [Learning Representations for Counterfactual Inference](https://mlsanity.com/paper/1605.03661/) (2016)
- [Continual learning with hypernetworks](https://mlsanity.com/paper/1906.00695/) (2019)
- [MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning](https://mlsanity.com/paper/1903.10258/) (2019)
- [The Benefit of Multitask Representation Learning](https://mlsanity.com/paper/1505.06279/) (2015)
- [Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms](https://mlsanity.com/paper/2101.10943/) (2021)
- [The Blessings of Multiple Causes](https://mlsanity.com/paper/1805.06826/) (2018)
- [A Brief Review of Hypernetworks in Deep Learning](https://mlsanity.com/paper/2306.06955/) (2023)
- [On Inductive Biases for Heterogeneous Treatment Effect Estimation](https://mlsanity.com/paper/2106.03765/) (2021)
- [Stochastic Hyperparameter Optimization through Hypernetworks](https://mlsanity.com/paper/1802.09419/) (2018)
- [Estimating Treatment Effects with Causal Forests: An Application](https://mlsanity.com/paper/1902.07409/) (2019)
- [Estimating Epistemic and Aleatoric Uncertainty with a Single Model](https://mlsanity.com/paper/2402.03478/) (2024)
- [Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search](https://mlsanity.com/paper/2010.15821/) (2020)
- [HyperINR: A Fast and Predictive Hypernetwork for Implicit Neural Representations via Knowledge Distillation](https://mlsanity.com/paper/2304.04188/) (2023)
- [A Framework for Controllable Pareto Front Learning with Completed Scalarization Functions and its Applications](https://mlsanity.com/paper/2302.12487/) (2023)
- [A Generative Model for Sampling High-Performance and Diverse Weights for Neural Networks](https://mlsanity.com/paper/1905.02898/) (2019)
- [Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects Estimation](https://mlsanity.com/paper/2305.15984/) (2023)
- [HyperNetworks with statistical filtering for defending adversarial examples](https://mlsanity.com/paper/1711.01791/) (2017)
- [Predictive Uncertainty Quantification with Compound Density Networks](https://mlsanity.com/paper/1902.01080/) (2019)
- [Adversarial De-confounding in Individualised Treatment Effects Estimation](https://mlsanity.com/paper/2210.10530/) (2022)
- [Effective Heterogeneous Federated Learning via Efficient Hypernetwork-based Weight Generation](https://mlsanity.com/paper/2407.03086/) (2024)
- [Quantum HyperNetworks: Training Binary Neural Networks in Quantum Superposition](https://mlsanity.com/paper/2301.08292/) (2023)
- [Hyper-CL: Conditioning Sentence Representations with Hypernetworks](https://mlsanity.com/paper/2403.09490/) (2024)
- [HCR-Net: A deep learning based script independent handwritten character recognition network](https://mlsanity.com/paper/2108.06663/) (2021)
- [Orthogonal Representation Learning for Estimating Causal Quantities](https://mlsanity.com/paper/2502.04274/) (2025)
- [Learning to Forget using Hypernetworks](https://mlsanity.com/paper/2412.00761/) (2024)
- [HypeRL: Hypernetwork-Based Reinforcement Learning for Control of Parametrized Dynamical Systems](https://mlsanity.com/paper/2501.04538/) (2025)
- [HyperCoil-Recon: A Hypernetwork-based Adaptive Coil Configuration Task Switching Network for MRI Reconstruction](https://mlsanity.com/paper/2308.04821/) (2023)

## Built on by (most cited first, 1 of 1)

- [Barycentric Fused Gromov-Wasserstein Balancing for Causal Inference under Multiple Treatments](https://mlsanity.com/paper/2608.22024/) (2026)

## 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.