Research Areas

Xie's current research focus is on causal inference and machine
learning. We develop methods that address a major challenge in causal
inference from observational data: the absence of perfect interventions.
One of our applied projects involves integrating causal models into
polygenic risk prediction for disease.

• Causal inference and machine learning
  Causal machine learning is related to the following research areas,
  which are often referred to with different names.
  1. Causal feature representation learning
  2. Causal graph learning
  3. Out-of-distribution generalization
  4. Treatment effect estimation, heterogenous treatment effects
  5. Counterfactual estimation
  6. Real World Data (RWD) or Real World Evidence (RWE)
  
  
• Feature representation learning of genomics and proteomics data
  
  
• Robust methods for stochastic gradient descent (SGD)
  
  

  Grant support

  
  
  
  
  

  Past Publications

  1. Cauchy combination test, published in JASA (Journal of the American Statistical Association)
     A test for weak and sparse alternatives with analytic p-value calculation and under arbitrary dependency structures
  2. Another paper in JASA
     "Accurate and Efficient P-value Calculation via Gaussian Approximation: a Novel Monte-Carlo Method"
  3. In the Annals of Applied Statistics 2018, Vol. 12, No. 1, 567-585.
     "Powerful test based on conditional effects for genome-wide screening"

  Google Scholar Citation

  Curriculum Vitae
  
  

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  Education

  1994: B.S. in Statistics

  Department of Probability and Statistics, Peking University

  1997: M.S. in Statistics

  Department of Probability and Statistics, Peking University

  2000: Ph.D. in Statistics

  Department of Statistics, University of California at Los Angeles

  
  

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  Research Group

  • Pengcheng Yang
  • Muye Liu
  • Haoze Li
  • Jinyong Lee
  
  

  Past group members in recent years

  • Donglai Chen, PhD 2019
  • Yaowu Liu, PhD 2017
  • Won Chul Song, Postdoc 2015-2017
  • Zhongyuan Chen, 2020-2022
  
  

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