Overview
Academic Appointments
- Assistant Professor of Biostatistics
Credentials & Experience
Education & Training
- MS, 2014 Biostatistics, Harvard University
- ScD, 2015 Biostatistics and Epidemiology, Harvard University
Honors & Awards
American Society of Human Genetics, Reviewer’s Choice Award, Top 10%, 2020.
Harvard Presidential Scholarship, Harvard University President’s Office, 2010-2015.
Research
Research Interests
- Biostatistical Methods
- Causal Inference
- Statistical Genetics and Genomics
Selected Publications
Sun, B., Liu, Z., Tchetgen Tchetgen, E., (2023). Semiparametric Efficient G-estimation with Invalid Instrumental Variables . Biometrika, asad011, https://doi.org/10.1093/biomet/asad011
Xu, Y., Liu, Z., Yao, J. (2023) An eigenvalue ratio approach to inferring population structure from whole genome sequencing data. Biometrics, 79 891–902. https://doi.org/10.1111/biom.13691
Liu, Z., Ye, T., Sun, B., Schooling, M., Tchetgen Tchetgen, E., (2022). Mendelian randomization mixed-scale treatment effect robust identification and estimation for causal inference Biometrics, 00, 1–12., https://doi.org/10.1111/biom.13735
Xu, S., Wang P., Fung, W.K., Liu, Z., (2022). A Novel Penalized Inverse-Variance Weighted Estimator for Mendelian Randomization with Applications to COVID-19 Outcomes. Biometrics, 00, 1–12. https://doi.org/10.1111/biom.13732
Xu, S., Liu, L. and Liu, Z. (2022) DeepMed: Semiparametric causal mediation analysis with debiased deep learning. The Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS), 35, pp. 28238-28251.
Wang, W.W., Xu, J., Schwartz, J., Baccarelli, A., Liu, Z. (2021). Causal mediation analysis with latent subgroups. Statistics in Medicine. 40(25): 5628– 5641
Liu, Z., Shen, J., Barfield, R., Schwartz, J., Baccarelli, A., Lin, X., (2021). Large-Scale Hypothesis Testing for Causal Mediation Effects with Applications in Genome-wide Epigenetic Studies. Journal of the American Statistical Association, 117(537), 67-81.
Liu, Z., Barnett, I., Lin, X. (2020). A comparison of principal component methods between multiple phenotype regression and multiple SNP regression in genetic association studies, The Annals of Applied Statistics, 14(1), pp.433-451.
Liu, Z. and Lin, X., 2019. A geometric perspective on the power of principal component association tests in multiple phenotype studies, Journal of the American Statistical Association, 114(527), pp.975-990.
Liu, Z. and Lin, X., 2018. Multiple phenotype association tests using summary statistics in genome-wide association studies. Biometrics, 74(1), pp.165-175.