1College of Science, China University of Petroleum in Beijing R.P. China
2School of Basic Medical Sciences, Capital Medical University, Beijing, China
American Journal of Medical Sciences and Medicine.
2013,
Vol. 1 No. 4, 55-61
DOI: 10.12691/ajmsm-1-4-2
Copyright © 2013 Science and Education PublishingCite this paper: Li Xiaotong, Li Sichen. Identification of Causal Effect with the Non-Compliance and Its
EM Algorithm.
American Journal of Medical Sciences and Medicine. 2013; 1(4):55-61. doi: 10.12691/ajmsm-1-4-2.
Correspondence to: Li Xiaotong, College of Science, China University of Petroleum in Beijing R.P. China. Email:
moonlane2009@126.comAbstract
Many practical studies in biology, medicine, behavior science and the social sciences seek to establish causal relationship between treatments and outcomes, rather than mere associations. In this paper, we use a graphical model to describe a causal graphical model and study its identification. For an unidentifiable model, we introduce covariates which are always observed into the model so that it becomes identifiable. We then give an identifiable condition of the causal graphical model and prove it mathematically. Finally, we give the

algorithm for the identifiable average causal effect of outcomes to the accepted treatment and give an example to illustrate this method and algorithm.
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