Lan Liu


Lan Liu
Associate Professor
Director of the Consulting Center
School of Statistics
University of Minnesota at Twin Cites


  • Harvard University, Boston, MA, USA, Aug 2015

    • Postdoctoral Fellow, Causal Inference Program Biostatistics & Epidemiology

      • Topic: Causal Inference with Instrumental Variable

      • Advisor: Eric Tchetgen Tchetgen, PhD

  • University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA, Jul 2013

    • Ph.D., Biostatistics

      • Dissertation Topic: Causal Inference in the Presence of Interference

      • Advisor: Michael G. Hudgens, PhD

  • University of Science and Technology of China, Hefei, Anhui, China, Jul 2010

    • B.S., Mathematics

Research Interests

  • Causal inference, missing data analysis, clinical trials, doubly robust inference, Bayesian analysis, surrogate outcomes, measurement error, mediation analysis, social network, personalized medicine, unmeasured confounder, statistical consulting.

Honors & Awards

  • ICSA Student Paper Award, 2013

    • International Chinese Statistics Association

  • ORISE Fellowship, 2012 Summer

    • Food and Drug Administration

  • Outstanding Students Scholarship

    • University of Science and Technology of China, 2006-2009

Selected Publication

  • L. Liu, M. G. Hudgens, “Large Sample Randomization Inference of Causal Effects in the Presence of Interference”, (2014) Journal of the American Statistical Association (JASA) Theory and Methods Section, 109(505):288-301.

  • Y. Yin*, L. Liu † , Z. Geng, P. Luo (2020) “Novel criteria to exclude the surrogate paradox and their optimalities”, Scandinavian Journal of Statistics, 47, 84-103

  • W. Li*, Y. Gu, L. Liu † (2020) “Demystifying a Class of Multiple Robust Estimators”, Biometrika, 207, 919-933

  • L. Liu, W. Li*, Z. Su, D. R. Cook, L. Vizioli, E. Yacoub, (2021) “The Efficiency Boosting via Envelope Chain for task-invoked fMRI study”, in press at Scandianvian Journal of Statistics

Course taught

  • Statistical Machine Learning, Spring 2021, 2022

  • Advanced Regression Techniques: linear, nonlinear and nonparametric methods, Fall 2019, 2020

  • Introduction to Statistical Learning, Spring 2017–2020

  • Applied Statistics, Fall 2016, Spring 2020, 2021

  • Statistical Analysis, Spring 2016

  • Statistical Consulting, Spring 2022

  • Literature Seminar, Fall 2016, Spring 2018


Introduction to Causal Inference




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Minneapolis 55455, USA
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