Treatment Effect Estimation for Graph-Structured Targets

TL;DR

This paper introduces GraphTEE, a novel framework for estimating treatment effects on graph-structured targets, effectively mitigating observational bias by focusing on confounding variables and providing theoretical bias reduction analysis.

Contribution

The study proposes GraphTEE, a new method tailored for treatment effect estimation on graph-structured data, with a focus on bias mitigation and confounding variable consideration.

Findings

GraphTEE outperforms baseline methods in synthetic datasets.

Theoretical analysis confirms improved bias mitigation.

Experimental results demonstrate effectiveness on semi-synthetic data.

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.