Richard Guo
Statistical Laboratory, DPMMS, University of Cambridge.
I am a Research Associate at the Statistical Laboratory of University of Cambridge. I am mentored by Prof. Rajen Shah.
In Spring 2022, I was a Richard M. Karp research fellow with the Causality program at the Simons Institute, UC Berkeley.
I earned my PhD in Statistics from University of Washington, Seattle in April 2021. My PhD advisor is Thomas S. Richardson. My doctoral thesis is on Likelihood analysis of causal models.
I work on
- Statistical foundations of causal inference: graphical models, semiparametric theory, robustness vs. efficiency trade-off
and, largely motivated by problems from causality, also
- Honest & flexible uncertainty quantification: model selection, irregularity, finite sample analysis, handling preprocessed data, randomization and derandomization, Bayes and empirical Bayes.
news
Jan 9, 2023 | New preprint on how to handle multiple data splitting and exchangeable p-values. |
Dec 17, 2022 | Presenting my work on inference for multiple data splitting and exchangeable p-values in CMStatistics 2022 at Kingâ€™s College London. |
Oct 31, 2022 | Paper Variable elimination, graph reduction and efficient g-formula is accepted to Biometrika. |
Aug 30, 2022 | A new review paper that clarifies misconceptions on confounder selection. |