1. Discussion of ’Estimating time-varying causal excursion effect in mobile health with binary outcomes’ by T. Qian et al Guo, F. Richard, Thomas S. Richardson, and James M. Robins March, 2021 [Abs] [arXiv]
  2. Empirical Bayes for large-scale randomized experiments: a spectral approach Guo, F. Richard, James McQueen, and Thomas S. Richardson Feb, 2020 [Abs] [arXiv]
  3. Efficient least squares for estimating total effects under linearity and causal sufficiency Guo, F. Richard, and Emilija Perković Aug, 2020 [Abs] [arXiv] [Slides] [Code]


  1. Chernoff-type concentration of empirical probabilities in relative entropy Guo, F. Richard, and Thomas S. Richardson IEEE Transactions on Information Theory 2020 [Abs] [arXiv] [HTML] [Code]
  2. On testing marginal versus conditional independence Guo, F. Richard, and Thomas S. Richardson Biometrika 2020 [Abs] [arXiv] [HTML] [Slides]
  3. How cognitive and reactive fear circuits optimize escape decisions in humans Song Qi, Demis Hassabis, Jiayin Sun, Guo, Fangjian, Nathaniel Daw, and Dean Mobbs Proceedings of the National Academy of Sciences (PNAS) 2018 [Abs] [HTML]
  4. Bounds of memory strength for power-law series Guo, Fangjian, Dan Yang, Zimo Yang, Zhi-Dan Zhao, and Tao Zhou Physical Review E 2017 [arXiv] [HTML]


  1. Minimal enumeration of all possible total effects in a Markov equivalence class Guo, F. Richard, and Emilija Perković In AISTATS 2021 [Abs] [arXiv] [HTML] [Poster]
  2. Boosting variational inference Guo, Fangjian, X Wang, K Fan, T Broderick, and D Dunson In NIPS Workshop on Advances in Approximate Bayesian Inference 2016 [Abs] [arXiv] [HTML] [Code]
  3. The Bayesian Echo Chamber: modeling social influence via linguistic accommodation Guo, Fangjian, Charles Blundell, Hanna Wallach, and Katherine Heller In AISTATS 2015 [arXiv] [HTML] [Code]
  4. Uncovering systematic bias in ratings across categories: a Bayesian approach Guo, Fangjian, and David Dunson In RecSys 2015 [HTML]
  5. Parallelizing MCMC with random partition trees Xiangyu Wang, Guo, Fangjian, Katherine Heller, and David Dunson In NIPS 2015 [Abs] [arXiv] [HTML] [Code]

notes and expository writings

  1. Causal Inference by using invariant prediction by Peters, Buhlmann and Meinshausen (2016) Guo, F. Richard 2018 [HTML] [Code]