معرفی
Melody Huang is an Assistant Professor of Political Science and Statistics & Data Science at Yale University. Her research focuses on developing robust statistical methods to estimate causal effects under real-world complexities, emphasizing generalizability of experiments, external validity, and sensitivity analysis. She holds a Ph.D. in Statistics from the University of California, Berkeley (2023) and a B.A. from UCLA (2017). Prior to Yale, she was a Postdoctoral Fellow at Harvard under Kosuke Imai.
Key research interests include quantitative methods, causal inference, survey methodology, and addressing noncompliance in experiments. Her work bridges statistical theory with practical applications in policy evaluation and social sciences. Recent contributions address subgroup analysis in causal effects, minimax regret estimation, and frameworks for evaluating AI-driven decision-making.
Teaching includes courses on statistical inference and causal methods at Yale. Her publications appear in top journals like Biometrika, Annals of Applied Statistics, and Political Analysis. She actively develops statistical software for methodological advancements.
