Structure Learning of Causal Bayesian Networks: A Survey

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  • Technical report TR11-01. Causality is a fundamental concept in reasoning. The effectiveness of many reasoning tasks depends on the understanding of the underlying cause-effect relationships. Therefore, the notion of causality has been explored in a wide range of disciplines. Causal discovery, however, was not modeled as a machine learning task until recently. Many learning approaches have recently been developed and applied to capture causation. The most frequently used approach among them is learning causal Bayesian networks (CBNs). A powerful calculus, capable of causal reasoning, has been formalized through CBNs. In this paper, we reviewed the fundamentals of learning causal structures using CBNs. We distinguished between observation and intervention, a crucial concept for learning CBNs. We reviewed some methods for learning from observational and interventional data. We have noted that, as a growing field of research, learning CBN structure is being investigated with increasingly difficult problems and possibilities are arising for incorporating it to other learning problems, such as active learning. | TRID-ID TR11-01

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    Attribution 3.0 International