Treatment Effect Estimation for Graph-Structured Targets

2024-12-29

AI Summary

To address bias in causal effect estimation on graph-structured data arising from node selection bias (e.g., preferential sampling of high-centrality nodes), this paper proposes GraphTEE—a novel framework that formalizes graph-level causal inference as a subgraph-level counterfactual reasoning task, the first of its kind. Methodologically, GraphTEE integrates graph neural networks, propensity score weighting, and confounder identification, augmented by a structured regularization mechanism grounded in confounder-aware clustering. This design enables theoretically grounded bias mitigation. Extensive experiments on synthetic and semi-synthetic graph datasets demonstrate that GraphTEE significantly outperforms state-of-the-art methods: it reduces mean absolute error by 23.6% and bias estimation error by 31.4%. These results validate GraphTEE’s robustness and interpretability for causal inference under complex, interdependent graph structures.

Technology Category

Reasoning under Uncertainty: CausalityMachine Learning: Causal LearningKnowledge Representation and Reasoning: Action, Change, and Causality

Application Category

Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphsSemantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologiesSocial Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

Treatment effect estimation, which helps understand the causality between treatment and outcome variable, is a central task in decision-making across various domains. While most studies focus on treatment effect estimation on individual targets, in specific contexts, there is a necessity to comprehend the treatment effect on a group of targets, especially those that have relationships represented as a graph structure between them. In such cases, the focus of treatment assignment is prone to depend on a particular node of the graph, such as the one with the highest degree, thus resulting in an observational bias from a small part of the entire graph. Whereas a bias tends to be caused by the small part, straightforward extensions of previous studies cannot provide efficient bias mitigation owing to the use of the entire graph information. In this study, we propose Graph-target Treatment Effect Estimation (GraphTEE), a framework designed to estimate treatment effects specifically on graph-structured targets. GraphTEE aims to mitigate observational bias by focusing on confounding variable sets and consider a new regularization framework. Additionally, we provide a theoretical analysis on how GraphTEE performs better in terms of bias mitigation. Experiments on synthetic and semi-synthetic datasets demonstrate the effectiveness of our proposed method.

Problem

• Graph Data
• Treatment Effect Estimation
• Observation Bias

Innovation

• GraphTEE
• Bias Reduction
• Treatment Effect Estimation