Richard Guo (he/him)
Alumni
Graduated in 2021 | |
ORCID iD | 0000-0002-2081-7398 |
Bio:
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.
Preprints
Variable elimination, graph reduction and efficient g-formula
F. Richard Guo, Emilija Perkovic, Andrea Rotnitzky
We study efficient estimation of an interventional mean associated with a point exposure treatment under a causal graphical model represented by a directed…
Rank-transformed subsampling: inference for multiple data splitting and exchangeable p-values
F. Richard Guo, Rajen D. Shah
Many testing problems are readily amenable to randomised tests such as those employing data splitting. However despite their usefulness in principle,…
Confounder selection via iterative graph expansion
F. Richard Guo, Qingyuan Zhao
Confounder selection, namely choosing a set of covariates to control for confounding between a treatment and an outcome, is arguably the most important step in…
Efficient least squares for estimating total effects under linearity and causal sufficiency
F. Richard Guo, Emilija Perkovic
Recursive linear structural equation models are widely used to postulate causal mechanisms underlying observational data. In these models, each variable equals…
Confounder Selection: Objectives and Approaches
F. Richard Guo, Anton Rask Lundborg, Qingyuan Zhao
Confounder selection is perhaps the most important step in the design of observational studies. A number of criteria, often with different objectives and…
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 Perkovic
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…
Discussion of 'Estimating time-varying causal excursion effect in mobile health with binary outcomes' by T. Qian et al
F. Richard Guo, Thomas S. Richardson, James M. Robins
We discuss the recent paper on "excursion effect" by T. Qian et al. (2020). We show that the methods presented have close relationships to others in the…
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…
Bounds of memory strength for power-law series
Fangjian Guo, Dan Yang, Zimo Yang, Zhi-Dan Zhao, Tao Zhou
Many time series produced by complex systems are empirically found to follow power-law distributions with different exponents $\alpha$. By permuting the…
Boosting Variational Inference
Fangjian Guo, Xiangyu Wang, Kai Fan, Tamara Broderick, David B. Dunson
Variational inference (VI) provides fast approximations of a Bayesian posterior in part because it formulates posterior approximation as an optimization…
Parallelizing MCMC with Random Partition Trees
Xiangyu Wang, Fangjian Guo, Katherine A. Heller, David B. Dunson
The modern scale of data has brought new challenges to Bayesian inference. In particular, conventional MCMC algorithms are computationally very expensive for…
The Bayesian Echo Chamber: Modeling Social Influence via Linguistic Accommodation
Fangjian Guo, Charles Blundell, Hanna Wallach, Katherine Heller
We present the Bayesian Echo Chamber, a new Bayesian generative model for social interaction data. By modeling the evolution of people's language usage over…
Predicting link directions via a recursive subgraph-based ranking
Fangjian Guo, Zimo Yang, Tao Zhou
Link directions are essential to the functionality of networks and their prediction is helpful towards a better knowledge of directed networks from incomplete…