A survey on causal reinforcement learning
While reinforcement learning (RL) achieves tremendous success in sequential decision-
making problems of many domains, it still faces key challenges of data inefficiency and the …
making problems of many domains, it still faces key challenges of data inefficiency and the …
Comprehensive review and empirical evaluation of causal discovery algorithms for numerical data
W Niu, Z Gao, L Song, L Li - arXiv preprint arXiv:2407.13054, 2024 - arxiv.org
Causal analysis has become an essential component in understanding the underlying
causes of phenomena across various fields. Despite its significance, existing literature on …
causes of phenomena across various fields. Despite its significance, existing literature on …
A survey on brain effective connectivity network learning
Human brain effective connectivity characterizes the causal effects of neural activities
among different brain regions. Studies of brain effective connectivity networks (ECNs) for …
among different brain regions. Studies of brain effective connectivity networks (ECNs) for …
A metaheuristic causal discovery method in directed acyclic graphs space
Causal discovery plays a vital role in the human understanding of the world. Searching a
directed acyclic graph (DAG) from observed data is one of the most widely used methods …
directed acyclic graph (DAG) from observed data is one of the most widely used methods …
Improving greedy local search methods by switching the search space
X Liu, X Gao, X Ru, X Tan, Z Wang - Applied Intelligence, 2023 - Springer
Bayesian networks play a vital role in human understanding of the world. Finding a precise
equivalence class of a Bayesian network is an effective way to represent causality. However …
equivalence class of a Bayesian network is an effective way to represent causality. However …
Inferring effective connectivity networks from fMRI time series with a temporal entropy-score
Inferring brain-effective connectivity networks from neuroimaging data has become a very
hot topic in neuroinformatics and bioinformatics. In recent years, the search methods based …
hot topic in neuroinformatics and bioinformatics. In recent years, the search methods based …
Sufficient dimension reduction for average causal effect estimation
A large number of covariates can have a negative impact on the quality of causal effect
estimation since confounding adjustment becomes unreliable when the number of …
estimation since confounding adjustment becomes unreliable when the number of …
Testing Conditional Independence Between Latent Variables by Independence Residuals
Conditional independence (CI) testing is an important problem, especially in causal
discovery. Most testing methods assume that all variables are fully observable and then test …
discovery. Most testing methods assume that all variables are fully observable and then test …
Recognizing Conditional Causal Relationships about Emotions and Their Corresponding Conditions
The study of causal relationships between emotions and causes in texts has recently
received much attention. Most works focus on extracting causally related clauses from …
received much attention. Most works focus on extracting causally related clauses from …
基于观测数据的时间序列因果推断综述
曾泽凡， 陈思雅， 龙洗， 金光 - 大数据, 2023 - infocomm-journal.com
摘要数据存储量的扩大和计算能力的提升为基于观测数据推断时间序列的因果关系开辟了新
途径. 在时间序列因果推断的基本性质和研究现状的基础上, 系统梳理了5 …
途径. 在时间序列因果推断的基本性质和研究现状的基础上, 系统梳理了5 …