Qijia He

I am a PhD candidate in the Department of Statistics at the University of Washington, where I am co-advised by professor Alex Luedtke and Bo Zhang. I am also very fortunate to collaborate with professor Yingqi Zhao, Ting Ye, and Fei Gao.

Previously, I received my MS degree in Statistics at the University of Washington in 2023. Before graduate school, I obtained my BS degree in Statistics from Sun Yat-sen University in 2021.

Contact me: heqj3@uw.edu

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Selected Research

I have broad research interests in causal inference and machine learning. Specifically, I have been working on projects about individualized optimal decision-making, assessing variable importance for heterogeneous treatment effects, mediation analysis, and generalizability in causal inference.

Role of placebo samples in observational studies
Ting Ye, Qijia He, Shuxiao Chen, Bo Zhang
Journal of Causal Inference, 2025
supplement

We proposed a framework for using placebo samples in observational studies to detect and correct for unmeasured confounding bias. It develops identification assumptions and estimation methods—including regression, weighting, and doubly robust approaches—and validates them through simulations and an applied case study on tax credits and infant health.

Generalizing the Intention-to-Treat Effect of an Active Control from Historical Placebo-Controlled Trials: A Case Study of the Efficacy of Daily Oral TDF/FTC in the HPTN 084 Study
Qijia He, Fei Gao, Oliver Dukes, Sinead Delany-Moretlwe, Bo Zhang*
Journal of the American Statistical Association, 2024
code / supplement

We developed a potential outcomes framework to estimate the ITT effect of an active control versus placebo in active-controlled trials, using historical placebo-controlled data. Our method enables ITT estimation when the placebo arm is unavailable and accounts for unmeasured confounders using instrumental variables.

Estimating individualized treatment rules by optimizing the adjusted probability of a longer survival
Qijia He, Shixiao Zhang, Michael L LeBlanc, Yingqi Zhao*
Statistical Methods in Medical Research, 2024
code / supplement

We introduced a new criterion for individualized treatment rules based on the adjusted probability of longer survival, offering a clear and clinically relevant objective. Our method, optimal adjusted probability learning, constructs the best treatment rule by maximizing this nonparametric survival benefit.

Presentations

Generalizing the Intention-to-Treat Effect of an Active Control from Historical Placebo-Controlled Trials

- The Translational Data Science Integrated Research Center Retreat. Kirkland, WA, 2023.

- 20th Annual STI & HIV Research Symposium. Seattle, WA, 2023.

- American Causal Inference Conference. Seattle, WA, 2024

- Joint Statistical Meetings. Portland, OR, 2024.

Approximate Bayesian Computation (ABC)-Calibrated Microsimulation Model for Predicting HIV-1 Prevention Efficacy of Broadly Neutralizing Antibodies

- HVTN Africa Regional Meeting. Cape Town, South Africa, 2024.

Teaching Experience

clean-usnob Department of Statistics, University of Washington

Teaching Assistant in STAT 311 Elements of Statistical Methods (Winter 2024)

clean-usnob Academic tutoring center, School of Mathematics, Sun Yat-sen University

Tutor in Mathematical analysis (Fall 2018)

clean-usnob TAL Education Group

Teaching Assistant in primary-school Olympiad Mathematics (2017-2018)