+ Welcome!

mail_outline Email: melody.huang@yale.edu

Twitter: @melodyyhuang

I'm currently an Assistant Professor of Political Science and Statistics & Data Science at Yale. My research broadly focuses on developing robust statistical methods to credibly estimate causal effects under real-world complications.

Before this, I was a Postdoctoral Fellow at Harvard, working with Kosuke Imai. I received my Ph.D. in Statistics at the Unversity of California, Berkeley, where I was fortunate to be advised by Erin Hartman.


Recent News

  • [June 2025]
My paper with Dan Soriano and Sam Pimentel on design sensitivity for weighted observational studies is forthcoming in the Journal of the Royal Statistical Society: Series A!
  • [June 2025]
My paper with Zhongren Chen on generalizing complier average causal effects is now available on ArXiv (link).
  • [May 2025]
My paper with with Eli Ben-Michael, Matthew McHugh, and Luke Keele on sensitivity analysis for clustered observational studies is now available on ArXiv (link).
  • [Feb. 2025]
My paper with Tiffany Tang and Ana Kenney on estimating interpretable subgroups with causal distillation trees is now available on ArXiv (link).
  • [Dec. 2024]
My paper with Yi Zhang and Kosuke Imai on minimax regret estimation for generalizing heterogeneous treatment effects is now available on ArXiv (link).

+ Research

Pre-Prints


Generalizing causal effects with noncompliance: Application to deep canvassing experiments
with Zhongren Chen

ArXiv

Sensitivity analysis for clustered observational studies with an application to the effectiveness of Magnet nursing hospitals
with Eli Ben-Michael, Matthew McHugh and Luke Keele

ArXiv

Distilling heterogeneous treatment effects: Stable subgroup estimation in causal inference
with Tiffany Tang and Ana Kenney


Minimax regret estimation for generalizing heterogeneous treatment effects with multisite data
with Yi Zhang and Kosuke Imai

ArXiv

Does AI help humans make better decisions? A statistical evaluation framework for experimental and observational studies
with Eli Ben-Michael, D. James Greiner, Kosuke Imai, Zhichao Jiang and Sooahn Shin

ArXiv

Publications


Design sensitivity and its implication on weighted observational studies
with Dan Soriano and Sam Pimentel
Journal of the Royal Statistical Society: Series A (2025+)

ArXiv

Overlap violations in external validity
Annals of Applied Statistics (2025)

ArXiv

Variance-based sensitivity analysis for weighted estimators result in more informative bounds
with Sam Pimentel
Biometrika (2025)

Article

Towards generalizing inferences from trials to target populations
with Harsh Parikh
Harvard Data Science Review (2024)

Article

Sensitivity analysis for the generalization of experimental results
Journal of the Royal Statistical Society: Series A (2024)

Article

Leveraging population outcomes to improve the generalization of experimental results
with Naoki Egami, Erin Hartman and Luke Miratrix
Annals of Applied Statistics (2024)

Article

Improving precision in the design and analysis of experiments with non-compliance
with Erin Hartman
Political Science Research and Methods (2023)

ArticleCode

Sensitivity analysis for survey weighting
with Erin Hartman
Political Analysis (2023)


Higher moments for optimal balance weighting in causal estimation
with Brian Vegetabile, Lane Burgette, Claude Setodji and Beth Ann Griffin
Epidemiology (2022)

Article

+ Teaching

Yale University

  • PLSC 500: Foundations of Statistical Inference (Fall 2024 - Present)
  • PLSC 503/S&DS 614: Causal Inference (Spring 2025 - Present)

University of California, Berkeley (Graduate Student Instructor)

  • STAT 232: Experimental Design (Spring 2023)
  • POLI SCI 236B: Quantitative Methodology in the Social Sciences (Spring 2022)

University of California, Los Angeles (Teaching Assistant)

  • STAT 100C: Linear Models (Spring 2019)
  • ECON 412: Fundamentals of Big Data (Spring 2019)