Causal Inference | Machine Learning

Research Focus

Currently I work in the fields of Tree-based Machine Learning with unbiased split for more reliable inference, Bayesian inference, non-parametric Bayesian for causal inference in observation study background. I am particularly interested in understanding various forms of population heterogeneity.


  • PyGUIDE[Current]: A Python Library with C++ extension for GUIDE algorithm working in Causal Inference.
  • Bayesian Raking[past]: A Bayesian framework for traditional raking framework, joint work with Professor Yajuan Si.

Other Interest

I am passionated in new technology and machine learning, especially in reinforcement learning and deep learning. I am also interested in computational question related with parallel algorithm.