As a PhD student at Dartmouth College, I work on advancing the foundations of causal machine learning and designing data fusion algorithms for health and social applications.

I am currently a member of Professor Bijan Mazaheri’s lab where I study the intersection of machine learning and causal inference. Our group focuses on challenges that arise when synthesizing information from multiple datasets, modalities, and batches into machine learning and AI models. Before moving to Hanover, I spent two years working as a Data Scientist and Data Analyst at a tech company and several AI startups. I hold an MS from both Cornell University and Boston University, and a BA from Rutgers University. I was fortunate to be part of Platform Governance Research Lab, advised by Professor Marshall Van Alstyne at BU. I was also privileged to collaborate with Professor AmirEmad Ghassami from the Department of Mathematics and Statistics and Professor Julia R. Köhler from Harvard Medical School on projects addressing community health issues using causal inference.

[Research Interest]

Data Fusion and Inference. One of the core challenges in social science and public policy research lies in integrating data from heterogeneous domains with differing covariates and distributions. I am interested in developing principled methods to integrate heterogeneous datasets across domains with differing covariates and distributions, improving causal inference and generalizable insights.
Causal Inference and Neural Networks. My current work explores how causal inference frameworks can be combined with neural network architectures to enhance model interpretability and robustness. This includes studying how representation learning can capture causal structures and how causal reasoning can guide deep learning models toward more reliable decision-making.
AI + Social Science/Public Health. How can we leverage AI to promote health equity and gain deeper insights into human behavior? How can insights from human cognition help us design better AI systems?

🔥 News

2025.04: 🎉🎉 Our work “Causal Data Fusion for Panel Data without Pre-Intervention Period” has been accepted to IC²S² ‘25! See you all in Sweden.
2025.03: 🗣️🎤 Presented “Causal Data Fusion for Panel Data without Pre-Intervention Period” at the ENAR Spring Meeting.
2024.10: 🎉🎉 My first preprint “Causal Data Fusion for Panel Data without Pre-Intervention Period” is out!.
2024.03: 🎉🎉 Our work has been accepted to IC²S² ‘24 oral!

📝 Publications

Causal Data Fusion for Panel Data without Pre-Intervention Period
Zou Yang, Seung Hee Lee, Julia R. Köhler, AmirEmad Ghassami
Improving the Governance of Digital Platforms with Interactive Marketplace Experiment (IC²S² '24 Oral)
Swapneel S Mehta, Sverre Wiedswang, Zou Yang, Nina Mazar, Marshall Van Alstyne.

💻 Projects

LLMs for Misinformation Detection
Zou Yang – This project leverages large language models (LLMs) for fake news detection. The goal of this project is to see the performance of LLMs on task of fake news detection.

🎖 Honors and Awards

2023.09 Scholarships ($25,000), Boston University
2017 Omicron Delta Epsilon Economics Honor Society, Rutgers University

💬 Presentation

2024.05, ENAR Spring Meeting， New Orleans, LA
2024.04, New England Student Research Symposium on Statistics and Data Science

🏞️ Hobbies

At my leisure time, I enjoy practicing yoga(meditation), playing guita, snowboarding and drinking coffee. I am also a certified yoga teacher (RYT-200).