@inproceedings{yu_online_2010,
	title = {Online causal discovery},
	doi = {10.1109/COGINF.2010.5599825},
	abstract = {The standard causal discovery assumes that all variables are available from the beginning. In this paper, we consider an untouched scenario in which not all variables are available in advance. We call this scenario online causal discovery which assumes that the target of interest is given in advance while the other variables are unknown. With this situation, an online algorithm is presented which consists of two phases: online growing and online shrinking phase. Experimental results validate our algorithms compared with a state-of-the-art standard algorithm of causal discovery.},
	booktitle = {9th {IEEE} {International} {Conference} on {Cognitive} {Informatics} ({ICCI}'10)},
	author = {Yu, Kui and Wu, Xindong and Wang, Hao},
	month = jul,
	year = {2010},
	keywords = {Algorithm design and analysis, Bayesian methods, Bayesian network, causal discovery, Classification algorithms, Heuristic algorithms, Markov processes, Measurement, online causal discovery, Probability distribution},
	pages = {667--671},
	file = {IEEE Xplore Abstract Record:/Users/soumikp/Zotero/storage/7ZDLVGEG/5599825.html:text/html},
}

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