1 citations · 2 across the 9 of their papers we have counts for
9 papers
CDFM: Towards a General-Purpose Causal Discovery Foundation Model
Jie Qiao, Ruichu Cai, Zijian Li +6
Causal discovery, the process of recovering underlying causal structures from observational data, is a fundamental pursuit across scientific disciplines. Over the past decades, num…
Causal View of Time Series Imputation: Some Identification Results on Missing Mechanism
Ruichu Cai, Kaitao Zheng, Junxian Huang +4
Time series imputation is one of the most challenge problems and has broad applications in various fields like health care and the Internet of Things. Existing methods mainly aim t…
Efficient Traffic State Prediction With Dynamic Joint Spatio-Temporal Relation Inference
Zhifeng Hao, Kai Hu, Juncai Zhang +2
Traffic prediction is difficult due to the complex interplay of temporal evolution, spatial interactions, and delayed spatio-temporal propagation over road networks. Existing metho…
Learning Discrete Latent Variable Structures with Tensor Rank Conditions
Zhengming Chen, Ruichu Cai, Feng Xie +5
Unobserved discrete data are ubiquitous in many scientific disciplines, and how to learn the causal structure of these latent variables is crucial for uncovering data patterns. Mos…
HateDebias: On the Diversity and Variability of Hate Speech Debiasing
Hongyan Wu, Zhengming Chen, Zijian Li +4
Hate speech frequently appears on social media platforms and urgently needs to be effectively controlled. Alleviating the bias caused by hate speech can help resolve various ethica…
Automating the Selection of Proxy Variables of Unmeasured Confounders
Feng Xie, Zhengming Chen, Shanshan Luo +3
Recently, interest has grown in the use of proxy variables of unobserved confounding for inferring the causal effect in the presence of unmeasured confounders from observational da…
Causal Discovery from Poisson Branching Structural Causal Model Using High-Order Cumulant with Path Analysis
Jie Qiao, Yu Xiang, Zhengming Chen +2
Count data naturally arise in many fields, such as finance, neuroscience, and epidemiology, and discovering causal structure among count data is a crucial task in various scientifi…
Nonstationary Time Series Forecasting via Unknown Distribution Adaptation
Zijian Li, Ruichu Cai, Zhenhui Yang +6
As environments evolve, temporal distribution shifts can degrade time series forecasting performance. A straightforward solution is to adapt to nonstationary changes while preservi…
Identification of Causal Structure in the Presence of Missing Data with Additive Noise Model
Jie Qiao, Zhengming Chen, Jianhua Yu +2
Missing data are an unavoidable complication frequently encountered in many causal discovery tasks. When a missing process depends on the missing values themselves (known as self-m…