Richard Guo (he/him)

PhD Student

UW Box Number 354322
Homepage Personal Home Page 
ORCID iD  0000-0002-2081-7398 

Richard Guo is a Ph.D. candidate in the Department of Statistics advised by Thomas Richardson. He is interested in non-standard statistical problems posed by causal inference, including irregularity, weak signal and non-identifiability. Previously, he studied computer science at Duke University and MIT. 


Chernoff-type Concentration of Empirical Probabilities in Relative Entropy
F. Richard Guo, Thomas S. Richardson
We study the relative entropy of the empirical probability vector with respect to the true probability vector in multinomial sampling of $k$ categories, which,…

Minimal enumeration of all possible total effects in a Markov equivalence class
F. Richard Guo, Emilija Perković
In observational studies, when a total causal effect of interest is not identified, the set of all possible effects can be reported instead. This typically…

Efficient Least Squares for Estimating Total Effects under Linearity and Causal Sufficiency
F. Richard Guo, Emilija Perković
Recursive linear structural equation models are widely used to postulate causal mechanisms underlying observational data. In these models, each variable equals…

Empirical Bayes for Large-scale Randomized Experiments: a Spectral Approach
F. Richard Guo, James McQueen, Thomas S. Richardson
Large-scale randomized experiments, sometimes called A/B tests, are increasingly prevalent in many industries. Though such experiments are often analyzed via…

On Testing Marginal versus Conditional Independence
F. Richard Guo, Thomas S. Richardson
We consider testing marginal independence versus conditional independence in a trivariate Gaussian setting. The two models are non-nested and their…