Shi, W., Zhao, A., and Liu, H. (2024). Rerandomization and covariate adjustment in split-plot designs. Journal of Business & Economic Statistics, https://doi.org/10.1080/07350015.2024.2429464
Zhao, A. and Ding, P. (2024). No star is good news: A unified look at rerandomization based on p-values from covariate balance tests. Journal of Econometrics 241, 105724.
Zhao, A., Ding, P., and Li, F. (2024). Covariate adjustment in randomized experiments with missing outcomes and covariates. Biometrika 111, 1413--1420.
Zhao, A. and Ding, P. (2024). To adjust or not to adjust? Estimating the average treatment effect in randomized experiments with missing covariates. Journal of American Statistical Association 119, 450--460.
Zhao, A. and Ding, P. (2023). A randomization-based theory for preliminary testing of covariate balance in controlled trials. Statistics in Biopharmaceutical Research 16, 498--511.
Zhao, A. and Ding, P. (2023). Covariate adjustment in multi-armed, possibly factorial experiments. Journal of the Royal Statistical Society, Series B (Statistical Methodology) 85， 1--23.
Zhao, A. and Ding, P. (2022). Regression-based causal inference with factorial experiments: estimands, model specifications and design-based properties. Biometrika, 109, 799--815.
Zhao, A. and Ding, P. (2022). Reconciling design-based and model-based causal inferences for split-plot experiments. The Annals of Statistics, 50, 1170-1192.
Zhao, A., Lee, Y., Small, D., and Karmaker, B. (2022). Evidence Factors from Multiple, Possibly Invalid, Instrumental Variables. The Annals of Statistics, 50, 1266-1296.
Zhao, A. and Ding, P. (2021). Covariate-adjusted Fisher randomization tests for the average treatment effect. Journal of Econometrics, 225, 278-294.
Zhao, A., Ding, P., Mukerjee, R. and Dasgupta, T. (2018). Randomization-based causal inference from split-plot designs. The Annals of Statistics, 46, 1876-1903.
Li, W. V., Zhao, A., Zhang, S., and Li, J. J. (2018). MSIQ: joint modeling of multiple RNA-seq samples for accurate isoform quantification. The Annals of Applied Statistics, 12, 510-539.
Zhao, A., Feng, Y., Wang, L., and Tong X. (2016). Neyman--Pearson classification under high-dimensional settings. The Journal of Machine Learning Research, 17, 7469-7507.
Xu, Y.*, Zhao, A.*, and Peng, D. (2024). Factorial Difference in Differences. https://arxiv.org/pdf/2407.11937. (*: equal contribution)