# GraphITE: Estimating Individual Effects of Graph-structured Treatments

arXiv: https://arxiv.org/abs/2009.14061 · HTML: https://mlsanity.com/paper/2009.14061/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2020.

## Built on by (most cited first, 18 of 18)

- [Causal Effect Inference for Structured Treatments](https://mlsanity.com/paper/2106.01939/) (2021)
- [Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimation](https://mlsanity.com/paper/2202.01336/) (2022)
- [Zero-shot causal learning](https://mlsanity.com/paper/2301.12292/) (2023)
- [Reliable Off-Policy Learning for Dosage Combinations](https://mlsanity.com/paper/2305.19742/) (2023)
- [Causal Effect Estimation: Recent Advances, Challenges, and Opportunities](https://mlsanity.com/paper/2302.00848/) (2023)
- [Comparison of meta-learners for estimating multi-valued treatment heterogeneous effects](https://mlsanity.com/paper/2205.14714/) (2022)
- [A Survey of Deep Causal Models and Their Industrial Applications](https://mlsanity.com/paper/2209.08860/) (2022)
- [Contrastive representations of high-dimensional, structured treatments](https://mlsanity.com/paper/2411.19245/) (2024)
- [DeCaf: A Causal Decoupling Framework for OOD Generalization on Node Classification](https://mlsanity.com/paper/2410.20295/) (2024)
- [KANITE: Kolmogorov-Arnold Networks for ITE estimation](https://mlsanity.com/paper/2503.13912/) (2025)
- [Causal Representation Learning for Generalisable Recommendation](https://mlsanity.com/paper/2605.27043/) (2026)
- [I See, Therefore I Do: Estimating Causal Effects for Image Treatments](https://mlsanity.com/paper/2412.06810/) (2024)
- [A Distributionally Robust Framework for Nuisance in Causal Effect Estimation](https://mlsanity.com/paper/2505.17717/) (2025)
- [Compositional Models for Estimating Causal Effects](https://mlsanity.com/paper/2406.17714/) (2024)
- [Educational Effects in Mathematics: Conditional Average Treatment Effect depending on the Number of Treatments](https://mlsanity.com/paper/2411.01498/) (2024)
- [Treatment Effect Estimation for Graph-Structured Targets](https://mlsanity.com/paper/2412.20436/) (2024)
- [Learning Treatment Representations for Downstream Instrumental Variable Regression](https://mlsanity.com/paper/2506.02200/) (2025)
- [Treatment Effect Estimation with Differentiated Networked Effect on Graph Data](https://mlsanity.com/paper/2605.24358/) (2026)

## Abstract (arXiv)

Outcome estimation of treatments for target individuals is an important foundation for decision making based on causal relations. Most existing outcome estimation methods deal with binary or multiple-choice treatments; however, in some applications, the number of treatments can be significantly large, while the treatments themselves have rich information. In this study, we considered one important instance of such cases: the outcome estimation problem of graph-structured treatments such as drugs. Owing to the large number of possible treatments, the counterfactual nature of observational data that appears in conventional treatment effect estimation becomes more of a concern for this problem. Our proposed method, GraphITE (pronounced "graphite") learns the representations of graph-structured treatments using graph neural networks while mitigating observation biases using Hilbert-Schmidt Independence Criterion regularization, which increases the independence of the representations of the targets and treatments. Experiments on two real-world datasets show that GraphITE outperforms baselines, especially in cases with a large number of treatments.