報(bào)告題目:Causal decision trees
報(bào)告人:Lin Liu
單位:University of South Australia
報(bào)告時(shí)間:2018-11-29上午11:00-11:40
報(bào)告地點(diǎn):翡翠湖校區(qū)科教大樓第一會(huì)議室
報(bào)告摘要：Uncovering causal relationships in data is a major objective of data analytics. Causal relationships are normally discovered with designed experiments, e.g. randomised controlled trials, which, however are expensive or infeasible to be conducted in many cases. Causal relationships can also be found using some well designed observational studies, but they require domain experts’ knowledge and the process is normally time consuming. Hence there is a need for scalable and automated methods for causal relationship exploration in data. Classification methods are fast and they could be practical substitutes for finding causal signals in data. However, classification methods are not designed for causal discovery and a classification method may find false causal signals and miss the true ones. In this talk, we will report a causal decision tree where nodes have causal interpretations.
報(bào)告人簡(jiǎn)介：
Dr Lin Liu is an associate professor at the School of Information Technology and Mathematical Sciences, University of South Australia. Her research interests include data mining and bioinformatics. She also has a background in protocol verification and network security analysis. Dr Liu has authored or co-authored over 80 publications. She was a PC chair of the Australasian Data Mining Conference (2013, 2014 and 2017), and a PC chair of KDD Workshop of Causal Discovery (2016-2018). She has served as reviewers for many prestigious conferences and journals in data mining and bioinformatics.
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