GraphITE: Estimating Individual Effects of Graph-structured Treatments

2020/09/29 by Shonosuke Harada, Hisashi Kashima, Harada, Shonosuke +1 · 2 citations
Economics, Econometrics and Finance · Mathematics · #Advanced Causal Inference Techniques #FOS: Computer and information sciences #Health Systems, Economic Evaluations, Quality of Life #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.2009.14061

openalex publication_date 2020/09/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Deep Convolutional Networks on Graph-Structured Data
• Causal Inference Using Potential Outcomes
• Adam: A Method for Stochastic Optimization
• Generative Adversarial Networks
• The central role of the propensity score in observational studies for causal effects
• Semi-Supervised Classification with Graph Convolutional Networks
• Neural Message Passing for Quantum Chemistry
• Inductive Representation Learning on Large Graphs
• SchNet – A deep learning architecture for molecules and materials
• Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks
• Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
• Generalization Bounds and Representation Learning for Estimation of\n Potential Outcomes and Causal Effects

Cited by

• Learning Treatment Representations for Downstream Instrumental Variable Regression
• A Distributionally Robust Framework for Nuisance in Causal Effect Estimation

Related

• Treatment Effect Estimation for Graph-Structured Targets
• Neural Networks with Causal Graph Constraints: A New Approach for Treatment Effects Estimation
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