Results 81 to 90 of about 2,139,585 (163)

Dual Likelihood for Causal Inference under Structure Uncertainty [PDF]

PMLR 236:1-17, 2024
Knowledge of the underlying causal relations is essential for inferring the effect of interventions in complex systems. In a widely studied approach, structural causal models postulate noisy functional relations among interacting variables, where the underlying causal structure is then naturally represented by a directed graph whose edges indicate ...
arxiv  

An Introduction to Causal Discovery [PDF]

arXiv
In social sciences and economics, causal inference traditionally focuses on assessing the impact of predefined treatments (or interventions) on predefined outcomes, such as the effect of education programs on earnings. Causal discovery, in contrast, aims to uncover causal relationships among multiple variables in a data-driven manner, by investigating ...
arxiv  

Can LLMs Leverage Observational Data? Towards Data-Driven Causal Discovery with LLMs [PDF]

Causal-NeSy @ ESWC 2025
Causal discovery traditionally relies on statistical methods applied to observational data, often requiring large datasets and assumptions about underlying causal structures. Recent advancements in Large Language Models (LLMs) have introduced new possibilities for causal discovery by providing domain expert knowledge.
arxiv  

Does the Rabbit's Foot Actually Work? The Causal Effect of Foreign Ownership on Firm Productivity in Three ASEAN Countries

Jurnal Teknik Industri, 2015
Voluminous studies have examined the relationship between foreign ownership and firm productivity. Two general patterns emerge at the empirical level: they are essentially correlational and results are mixed.
Inggrid Inggrid
doaj  

Estimating the Heterogeneous Causal Effects of Parent–Child Relationships among Chinese Children with Oppositional Defiant Symptoms: A Machine Learning Approach

Behavioral Sciences
Oppositional defiant symptoms are some of the most common developmental symptoms in children and adolescents with and without oppositional defiant disorder.
Haiyan Zhou, Fengkai Han, Ruoxi Chen, Jiajin Huang, Jianhui Chen, Xiuyun Lin   +5 more
doaj   +1 more source

Causality of immune cells on primary sclerosing cholangitis: a bidirectional two-sample Mendelian randomization study

Frontiers in Immunology
BackgroundObservational studies have indicated that immune dysregulation in primary sclerosing cholangitis (PSC) primarily involves intestinal-derived immune cells.
Pu Wu, Pu Wu, Pu Wu, Sinan Xie, Sinan Xie, Sinan Xie, Yunshi Cai, Yunshi Cai, Yunshi Cai, Hu Liu, Hu Liu, Hu Liu, Yinghao Lv, Yinghao Lv, Yinghao Lv, Ying Yang, Ying Yang, Ying Yang, Yucheng He, Yucheng He, Yucheng He, Bangjie Yin, Bangjie Yin, Bangjie Yin, Tian Lan, Tian Lan, Tian Lan, Hong Wu, Hong Wu, Hong Wu   +29 more
doaj   +1 more source

Moments of Causal Effects [PDF]

arXiv
The moments of random variables are fundamental statistical measures for characterizing the shape of a probability distribution, encompassing metrics such as mean, variance, skewness, and kurtosis. Additionally, the product moments, including covariance and correlation, reveal the relationships between multiple random variables.
arxiv  

Dynamic Causal Structure Discovery and Causal Effect Estimation [PDF]

arXiv
To represent the causal relationships between variables, a directed acyclic graph (DAG) is widely utilized in many areas, such as social sciences, epidemics, and genetics. Many causal structure learning approaches are developed to learn the hidden causal structure utilizing deep-learning approaches.
arxiv  

Mendelian Randomization Analysis Identifies Causal Effects of Multi-Site Chronic Pain on Obstructive Sleep Apnea

Nature and Science of Sleep
Zuxing Wang,1,* Lili Chen,1,* Ruishi Kang,2,* Zhuowei Li,2 Jiangang Fan,2 Yi Peng,3,* Yunqi He,4,5 Xiaolong Zhao2 1Sichuan Provincial Center for Mental Health, Sichuan Provincial People’s Hospital, School of Medicine, University of ...
Wang Z, Chen L, Kang R, Li Z, Fan J, Peng Y, He Y, Zhao X   +7 more
doaj  

Causal DAG Summarization (Full Version) [PDF]

arXiv
Causal inference aids researchers in discovering cause-and-effect relationships, leading to scientific insights. Accurate causal estimation requires identifying confounding variables to avoid false discoveries. Pearl's causal model uses causal DAGs to identify confounding variables, but incorrect DAGs can lead to unreliable causal conclusions. However,
arxiv