A study on causal rule discovery with PC algorithm

Author(s):  
B. Rama ◽  
P. Praveen ◽  
Harshit Sinha ◽  
Tanupriya Choudhury
Keyword(s):  
Crisis ◽  
2019 ◽  
Vol 40 (3) ◽  
pp. 157-165 ◽  
Author(s):  
Kevin S. Kuehn ◽  
Annelise Wagner ◽  
Jennifer Velloza

Abstract. Background: Suicide is the second leading cause of death among US adolescents aged 12–19 years. Researchers would benefit from a better understanding of the direct effects of bullying and e-bullying on adolescent suicide to inform intervention work. Aims: To explore the direct and indirect effects of bullying and e-bullying on adolescent suicide attempts (SAs) and to estimate the magnitude of these effects controlling for significant covariates. Method: This study uses data from the 2015 Youth Risk Behavior Surveillance Survey (YRBS), a nationally representative sample of US high school youth. We quantified the association between bullying and the likelihood of SA, after adjusting for covariates (i.e., sexual orientation, obesity, sleep, etc.) identified with the PC algorithm. Results: Bullying and e-bullying were significantly associated with SA in logistic regression analyses. Bullying had an estimated average causal effect (ACE) of 2.46%, while e-bullying had an ACE of 4.16%. Limitations: Data are cross-sectional and temporal precedence is not known. Conclusion: These findings highlight the strong association between bullying, e-bullying, and SA.


Author(s):  
Hala AlShamlan ◽  
Lamia Almoajil ◽  
Khawlah AlBabtain ◽  
Manal BinShalaan ◽  
Rand AlMahmoud ◽  
...  

2017 ◽  
Vol 658 ◽  
pp. 357-374 ◽  
Author(s):  
Brice Chardin ◽  
Emmanuel Coquery ◽  
Marie Pailloux ◽  
Jean-Marc Petit

2017 ◽  
Vol 88 ◽  
pp. 73-83 ◽  
Author(s):  
Tong Wang ◽  
Qi Han ◽  
Bauke de Vries
Keyword(s):  

2014 ◽  
Vol 532 ◽  
pp. 113-117
Author(s):  
Zhou Jin ◽  
Ru Jing Wang ◽  
Jie Zhang

The rotating machineries in a factory usually have the characteristics of complex structure and highly automated logic, which generated a large amounts of monitoring data. It is an infeasible task for uses to deal with the massive data and locate fault timely. In this paper, we explore the causality between symptom and fault in the context of fault diagnosis in rotating machinery. We introduce data mining into fault diagnosis and provide a formal definition of causal diagnosis rule based on statistic test. A general framework for diagnosis rule discovery based on causality is provided and a simple implementation is explored with the purpose of providing some enlightenment to the application of causality discovery in fault diagnosis of rotating machinery.


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