Conceptual Aspects of Causal Networks in an Applied ContextAzam Yazdani*, Akram Yazdani and Eric Boerwinkle
Human Genetics Center, UT Health School of Public Health, 1200 Pressler Street, Suite E-447, Houston, Texas, USA
- *Corresponding Author:
- Azam Mandana Yazdani
University of Texas Health Science Center
Houston-1200, Herman Pressler
Houston, Texas, United States
E-mail: [email protected]
Received Date: January 14, 2016; Accepted Date: February 10, 2016; Published Date: February 17, 2016
Citation: Yazdani A, Yazdani A, Boerwinkle E (2016) Conceptual Aspects of Causal Networks in an Applied Context. J Data Mining Genomics Proteomics 7:188. doi:10.4172/2153-0602.1000188
Copyright: © 2016 Yazdani A, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Making causal inference is conceptually straightforward in the setting of a randomized intervention, such as a clinical trial. However, in observational studies, which represent the majority of most large-scale epidemiologic studies, causal inference is complicated by confounding and lack of clear directionality underlying an observed association. In most large scale biomedical applications, causal inference is embodied in Directed Acyclic Graphs (DAG), which is an illustration of causal relationships (i.e., arrows) among the variables (i.e., nodes). A key concept for making causal inference in the context of observational studies is the assignment mechanism, whereby some individuals are treated and some are not. This perspective provides a structure for thinking about causal networks in the context of the assignment mechanism (AM). Estimation of effect sizes of the observed directed relationships is presented and discussed.