Open Access Peer-reviewed

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

Li Xiaotong1,, Li Sichen2

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, 1(4), 55-61. DOI: 10.12691/ajmsm-1-4-2
Published online: August 25, 2017


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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