Xiao Liu's research focuses on developing and applying statistical methods for causal inference in observational or randomized studies. This includes causal mediation analysis, causal inference with machine learning methods, longitudinal or clustered data analyses, study design (e.g., sample size calculation and statistical power analysis), and sensitivity analysis methods. Substantively, her research interests have so far focused on applications of statistical methods in education and health, including prevention and intervention programs for children, adolescents, and families, developmental disabilities, cancer care, and mental health.
Ph.D. in Quantitative Psychology, University of Notre Dame, 2022
M.S. in Applied Statistics, University of Notre Dame, 2020
B.S. in Statistics, Renmin University of China, 2017
Statistical methods for causal mediation analysis, causal inference with machine learning methods, longitudinal or clustered data analysis, study design (e.g., statistical power analysis)
Liu, X. (2025). Estimating causal mediation effects in multiple-mediator analyses with clustered data. Journal of Educational and Behavioral Statistics. doi:(accepted).
Liu, X., Eddy, J. M.. & Martinez, C. R.. (2025). Causal estimands and multiply robust estimation of mediated-moderation.
Multivariate Behavioral Research, Advanced Online Publ. (
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Liu, X. (2024). Propensity score weighting with missing data on covariates and clustered data structure.
Multivariate Behavioral Research, Advanced online publ. (
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Liu, X.., Zhang, Z.., Valentino, K.. & Wang, L.. (2024). The impact of omitting confounders in parallel process latent growth curve mediation models: Three sensitivity analysis approaches.
Structural Equation Modeling: A Multidisciplinary Journal,
31(1), 132–150. (
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Liu, X., Zhang, Z. & Wang, L. (2024). Detecting mediation effects with the Bayes factor: Performance evaluation and tools for sample size determination.
Psychological Methods. (
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Liu, X. & Wang, L. (2024). Causal mediation analysis with the parallel process latent growth curve mediation model.
Structural Equation Modeling: A Multidisciplinary Journal. (
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Liu, X. (2024). Assessing heterogeneous causal effects across clusters in partially nested designs.
Psychological Methods, Advanced Online Publ. (
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Liu, X., Liu, F., Miller-Graff, L., Howell, K. & Wang, L. (2023). Causal inference for treatment effects in partially nested designs.
Psychological Methods, Advanced online publ. (
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Liu, X., Zhang, Z. & Wang, L. (2023). Bayesian hypothesis testing of mediation: Methods and the impact of prior odds specifications.
Behavior Research Methods,
55(3), 1108–1120. (
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Liu, X. & Wang, L. (2021). The impact of measurement error and omitting confounders on statistical inference of mediation effects and tools for sensitivity analysis.
Psychological Methods,
26(3), 327–342. (
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Liu, X. & Wang, L. (2019). Sample size planning for detecting mediation effects: a power analysis procedure considering uncertainty in effect size estimates.
Multivariate Behavioral Research,
54(6), 822–839. (
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Outstanding Quantitative Dissertation Award, American Educational Research Association, Division D (2023)