26 citations · 38 across the 13 of their papers we have counts for
13 papers
Temporal Smoothness Doubly Robust Learning for Debiased Knowledge Tracing
Peilin Zhan, Wei Chen, Weilin Chen +2
Knowledge Tracing (KT) is fundamental to intelligent education systems, yet relies on educational logs that are selectively observed. The non-random nature of exercise recommendati…
CausalAgent: A Conversational Multi-Agent System for End-to-End Causal Inference
Jiawei Zhu, Wei Chen, Ruichu Cai
Causal inference holds immense value in fields such as healthcare, economics, and social sciences. However, traditional causal analysis workflows impose significant technical barri…
Temporal Latent Variable Structural Causal Model for Causal Discovery under External Interferences
Ruichu Cai, Xiaokai Huang, Wei Chen +2
Inferring causal relationships from observed data is an important task, yet it becomes challenging when the data is subject to various external interferences. Most of these interfe…
Identification of Causal Direction under an Arbitrary Number of Latent Confounders
Wei Chen, Linjun Peng, Zhiyi Huang +4
Recovering causal structure in the presence of latent variables is an important but challenging task. While many methods have been proposed to handle it, most of them require stric…
Causal-aware Large Language Models: Enhancing Decision-Making Through Learning, Adapting and Acting
Wei Chen, Jiahao Zhang, Haipeng Zhu +5
Large language models (LLMs) have shown great potential in decision-making due to the vast amount of knowledge stored within the models. However, these pre-trained models are prone…
An Identifiable Cost-Aware Causal Decision-Making Framework Using Counterfactual Reasoning
Ruichu Cai, Xi Chen, Jie Qiao +5
Decision making under abnormal conditions is a critical process that involves evaluating the current state and determining the optimal action to restore the system to a normal stat…
Identification of Causal Structure with Latent Variables Based on Higher Order Cumulants
Wei Chen, Zhiyi Huang, Ruichu Cai +2
Causal discovery with latent variables is a crucial but challenging task. Despite the emergence of numerous methods aimed at addressing this challenge, they are not fully identifie…
Causal-learn: Causal Discovery in Python
Yujia Zheng, Biwei Huang, Wei Chen +6
Causal discovery aims at revealing causal relations from observational data, which is a fundamental task in science and engineering. We describe , an open-so…
Time-Series Domain Adaptation via Sparse Associative Structure Alignment: Learning Invariance and Variance
Zijian Li, Ruichu Cai, Jiawei Chen +4
Domain adaptation on time-series data is often encountered in the industry but received limited attention in academia. Most of the existing domain adaptation methods for time-serie…
REST: Debiased Social Recommendation via Reconstructing Exposure Strategies
Ruichu Cai, Fengzhu Wu, Zijian Li +4
The recommendation system, relying on historical observational data to model the complex relationships among the users and items, has achieved great success in real-world applicati…
CCSL: A Causal Structure Learning Method from Multiple Unknown Environments
Wei Chen, Yunjin Wu, Ruichu Cai +2
Most existing causal structure learning methods assume data collected from one environment and independent and identically distributed (i.i.d.). In some cases, data are collected f…
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…
Time Series Domain Adaptation via Sparse Associative Structure Alignment
Ruichu Cai, Jiawei Chen, Zijian Li +6
Domain adaptation on time series data is an important but challenging task. Most of the existing works in this area are based on the learning of the domain-invariant representation…