Title: Adaptive causal inference and its applications

Date: Wednesday, October 30th

Time: 4PM EST

Location: Microsoft Team Meeting, Meeting ID 278 734 381 501, Join Link

Hang Wu

Machine Learning PhD Student

Biomedical Engineering
Georgia Institute of Technology

Committee

1 Dr. May D. Wang (Advisor), Biomedical Engineering, Georgia Tech & Emory University

2 Dr. Bo Dai, Computer Science & Engineering, Georgia Tech

3 Dr. Chao Zhang, Computer Science & Engineering, Georgia Tech

4 Dr. Duen Horng "Polo" Chau, Computer Science & Engineering, Georgia Tech

5 Dr. Blake Anderson, Emory University

Abstract

Causal inference is essential for understanding variable relationships and improving decision-making across domains such as policy analysis and biomedical research. In biomedical contexts, challenges like data scarcity, privacy regulations, and dataset heterogeneity complicate the development of robust causal models. This thesis introduces a novel framework for adaptive causal inference, leveraging meta-learning and transfer learning to enhance the transferability of knowledge and enable rapid adaptation to unseen data. 

We address two key causal inference tasks: causal effect estimation and causal graph discovery, proposing adaptive algorithms that generalize across multi-source data and improve inference accuracy in heterogeneous settings. Applications of these methods include predictive biomedical imaging models, fair classification and policy systems, and algorithms for inferring causes of death. This work advances the development of personalized and reliable decision-making systems in healthcare and other fields.