In this paper, we present a systematic and conceptual overview of methods for inferring gene regulatory networks from observational gene expression data. Further, we discuss two classic approaches to infer causal structures and compare them with contemporary methods by providing a conceptual categorization thereof. We complement the above by surveying global and local evaluation measures for assessing the performance of inference algorithms.
Keywords: Bayesian network; causal relations; directed acyclic graphs; gene regulatory networks; information-theory methods; reverse engineering; statistical inference.