Yuyao Wang

I am a final year PhD student in mathematics with a specialization in statistics at University of California San Diego, advised by Prof. Ronghui (Lily) Xu. Before graduate school, I recieved my bachelor’s degree in mathematics from Xi’an Jiaotong University, China. I also visited Georgia Institute of Technology and University of Alberta as a visiting student during my undergrad.

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Research

My methodology research interests include causal inference, survival analysis, missing data problems, semiparametric theory. I am broadly interested in different topics in causal inference and missing data problems, especially in selection bias, heterogeneous treatment effects, balancing weighting, continuous treatment, and continuous time treatment. I am also broadly interested in applications in health sciences, as well as social sciences such as education and psychology.

Publications and Preprints
  1. Yuyao Wang, Andrew Ying, Ronghui Xu. (2024) Doubly robust estimation under covariate-induced dependent left truncation. Biometrika, asae005, https://doi.org/10.1093/biomet/asae005 [pdf] [code] [R package]

    (This paper won the student paper competition award for 2023 Lifetime Data Science Conference)

  2. Yingwei Peng, Yuyao Wang, Ronghui Xu. (2023) Measures of explained variation under the mixture cure model for survival data. Statistics in Medicine, 42(3), 228-245. [pdf]
Working Papers
  1. Yuyao Wang, Andrew Ying, Ronghui Xu. (2024+) Learning treatment effects under covariate dependent left truncation and right censoring. On arXiv soon.
  2. Lon R. White, Yuyao Wang, Lenore Launer, Lucia Seale, Marla Berry, Margaret Flannagan, Thomas J. Montine, Ronghui Xu. (2024+) Midlife dietary selenium and Okinawan ancestry are independently associated with less severe Alzheimer neuropathology at autopsy in Japanese-American men. Submitted to Journal of Alzheimer's Disease.
R packages
  1. truncAIPW: Doubly Robust Estimation under Covariate-Induced Dependent Left Truncation
  2. aftR2: R-squared Measure under Accelerated Failure Time (AFT) Models
Presentations
  1. Learning treatment effects under covariate dependent left truncation and right censoring. Presentation at 2024 Joint Statistical Meetings (JSM).
  2. Doubly robust estimation of treatment effects under covariate dependent left truncation and right censoring. Poster at 2024 American Causal Inference Conference (ACIC).
  3. Learning Treatment Effects under Covariate Dependent Left Truncation and Right Censoring. Presentation at 2024 Southern California Applied Mathematics Symposium (COCAMS).
  4. Doubly Robust Estimation under Covariate-induced Dependent Left Truncation. Presentation at McGill Statistics Seminar (2023 Fall). [slides]
  5. Doubly Robust Estimation under Covariate-induced Dependent Left Truncation. Presentation at 2023 Joint Statistical Meetings (JSM). [slides]
  6. Doubly Robust Estimation under Covariate-induced Dependent Left Truncation. Presentation at 2023 Lifetime Data Science Conference (LiDS). [slides]
  7. Multiply Robust Estimation of Treatment Effect for Time-to-event Outcome under Dependent Left Truncation. Poster at 2023 American Causal Inference Conference (ACIC).
  8. Doubly Robust Estimation under Covariate-induced Dependent Left Truncation. Poster at 2023 Public Health Research Day at UCSD.
  9. Semiparametric Estimation for Non-randomly Truncated Data. Poster at 2022 American Causal Inference Conference (ACIC).
  10. Semiparametric Estimation for Non-randomly Truncated Data. Poster presented at 2022 Public Health Research Day at UCSD.

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