Cellular networks usually consist of numerous chemical species, such as DNA, RNA, proteins and small molecules, etc. Different biological tasks are generally performed by complex interactions of these species. As these interactions can rarely be directly measured, it is widely recognized that causal relationship identification is essential in understanding biological behaviors of a cellular network. Challenging issues here include not only the large number of interactions to be estimated, but also many restrictions on probing signals. The purposes of this study are to incorporate power law in cellular network identification, in order to increase accuracy of causal regulation estimations, especially to reduce false positive errors.