Bridging the gap between causal inference and graph machine learning
Abstract:
Recent years have witnessed rapid development in graph-based machine learning (ML) in various high-impact domains, especially those powered by effective graph neural networks (GNNs). Currently, the mainstream graph ML methods are based on statistical learning, e.g., utilizing the statistical correlations between node features, graph structure, and labels for node classification. However, statistical learning has been widely criticized for only capturing the superficial relations between variables in the data system, and consequently, rendering the lack of trustworthiness in real-world applications. Therefore, it is crucial to understand the causality in the data system and the learning process. Causal inference is the discipline that investigates the causality inside a system, for example, to identify and estimate the causal effect of a certain treatment (e.g., wearing a face mask) on an important outcome (e.g., COVID-19 infection). Involving the concepts and philosophy of causal inference into ML methods is often considered as a significant component of human-level intelligence and can serve as the foundation of artificial intelligence (AI). However, most traditional causal inference studies rely on strong assumptions, and focus on independent and identically distributed (i.i.d.) data, while causal inference on graphs is faced with many barriers in effectiveness and efficiency. Therefore, there is still a gap between causal inference and graph ML. In the proposed research, we aim to bridge this gap from different aspects.
Committee:
- Yangfeng Ji, Committee Chair (CS/SEAS/UVA)
- Jundong Li, Co-Advisor (CS, ECE/SEAS, SDS/UVA)
- Aidong Zhang, Co-Advisor (CS, BME/SEAS, SDS/UVA)
- Hongning Wang (CS/SEAS/UVA)
- Anil Vullikanti (CS, Biocomplexity/SEAS/UVA)
- Sheng Li (SDS/UVA)
- Emre Kiciman (Microsoft Research)