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Melody Huang is an Assistant Professor of Political Science and Statistics & Data Science at Yale. Her research sits at the intersection of statistics and political science and focuses on developing robust statistical methods to credibly estimate causal effects under real-world complications. Prior to Yale, she was a Wojcicki-Troper Data Science Initiative Postdoctoral Fellow at Harvard. She received her Ph.D. in Statistics from the University of California, Berkeley in 2023, where her dissertation was awarded the John T. Williams Prize by the Society for Political Methodology.

What do you do with Data Science?

My work in data science develops new methods for researchers to credibly estimate causal effects under real-world complications. My research has two primary focuses. My first research focus is on developing sensitivity tools for researchers to relax common identifying assumptions and evaluate the robustness of their estimated effects. My second research focus is on estimating externally valid causal effects in the presence of different distribution shifts. My work in these areas has been published in venues, such as Biometrika, Annals of Applied Statistics, and Journal of the Royal Statistical Society: Series A. My future work will focus on developing methods that allow researchers to evaluate, a priori, how to build better models and choose more effective estimation strategies to obtain distributionally robust estimates.

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