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American Journal of Medical Sciences and Medicine. 2013, 1(4), 55-61
DOI: 10.12691/ajmsm-1-4-2
Open AccessArticle

Identification of Causal Effect with the Non-Compliance and Its EM Algorithm

Li Xiaotong1, and Li Sichen2

1College of Science, China University of Petroleum in Beijing R.P. China

2School of Basic Medical Sciences, Capital Medical University, Beijing, China

Pub. Date: June 15, 2013

Cite this paper:
Li Xiaotong and 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


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.

the rubin causal model instrument variables graphical model identification non-compliance EM algorithm average causal effect

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