16 citations · 48 across the 5 of their papers we have counts for
9 papers
Latent Hierarchical Causal Structure Discovery with Rank Constraints
Biwei Huang, Charles Jia Han Low, Feng Xie +2
Most causal discovery procedures assume that there are no latent confounders in the system, which is often violated in real-world problems. In this paper, we consider a challenging…
FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders
Wei Chen, Kun Zhang, Ruichu Cai +4
We consider the problem of estimating a particular type of linear non-Gaussian model. Without resorting to the overcomplete Independent Component Analysis (ICA), we show that under…
Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation
Chaochao Lu, Biwei Huang, Ke Wang +3
Reinforcement learning (RL) algorithms usually require a substantial amount of interaction data and perform well only for specific tasks in a fixed environment. In some scenarios s…
Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs
Feng Xie, Ruichu Cai, Biwei Huang +3
Causal discovery aims to recover causal structures or models underlying the observed data. Despite its success in certain domains, most existing methods focus on causal relations b…
Domain Adaptation as a Problem of Inference on Graphical Models
Kun Zhang, Mingming Gong, Petar Stojanov +3
This paper is concerned with data-driven unsupervised domain adaptation, where it is unknown in advance how the joint distribution changes across domains, i.e., what factors or mod…
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models
Biwei Huang, Kun Zhang, Mingming Gong +1
In many scientific fields, such as economics and neuroscience, we are often faced with nonstationary time series, and concerned with both finding causal relations and forecasting t…