32 citations · 60 across the 9 of their papers we have counts for
9 papers
Robust Bayesian Dynamic Programming for On-policy Risk-sensitive Reinforcement Learning
Shanyu Han, Yangbo He, Yang Liu
We propose a novel framework for risk-sensitive reinforcement learning (RSRL) that incorporates robustness against transition uncertainty. We define two distinct yet coupled risk m…
Covariate Balancing Value Estimation for Optimal Individualized Treatment Rules
Yue Zhang, Shanshan Luo, Zhi Geng +1
Learning an optimal individualized treatment rule depends on reliable value comparisons across the candidate class. Standard doubly robust estimators are consistent when either the…
Identifiability of causal effects with non-Gaussianity and auxiliary covariates
Kang Shuai, Shanshan Luo, Yue Zhang +2
Assessing causal effects in the presence of unmeasured confounding is challenging. Although auxiliary variables, such as instrumental variables, are commonly used to identify causa…
On the Representation of Pairwise Causal Background Knowledge and Its Applications in Causal Inference
Zhuangyan Fang, Ruiqi Zhao, Yue Liu +1
Pairwise causal background knowledge about the existence or absence of causal edges and paths is frequently encountered in observational studies. Such constraints allow the shared…
A Local Method for Identifying Causal Relations under Markov Equivalence
Zhuangyan Fang, Yue Liu, Zhi Geng +2
Causality is important for designing interpretable and robust methods in artificial intelligence research. We propose a local approach to identify whether a variable is a cause of…
On Low Rank Directed Acyclic Graphs and Causal Structure Learning
Zhuangyan Fang, Shengyu Zhu, Jiji Zhang +3
Despite several advances in recent years, learning causal structures represented by directed acyclic graphs (DAGs) remains a challenging task in high dimensional settings when the…
Formulas for Counting the Sizes of Markov Equivalence Classes of Directed Acyclic Graphs
Yangbo He, Bin Yu
The sizes of Markov equivalence classes of directed acyclic graphs play important roles in measuring the uncertainty and complexity in causal learning. A Markov equivalence class c…
Supplement to "Reversible MCMC on Markov equivalence classes of sparse directed acyclic graphs"
Yangbo He, Jinzhu Jia, Bin Yu
This supplementary material includes three parts: some preliminary results, four examples, an experiment, three new algorithms, and all proofs of the results in the paper "Reversib…
Reversible MCMC on Markov equivalence classes of sparse directed acyclic graphs
Yangbo He, Jinzhu Jia, Bin Yu
Graphical models are popular statistical tools which are used to represent dependent or causal complex systems. Statistically equivalent causal or directed graphical models are sai…