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In this paper, we investigate the problem of discovering both direct and indirect discrimination from the historical data, and removing the discriminatory effects before the data is used for predictive analysis (e.g., building classifiers). The main drawback of existing methods is that they cannot distinguish the part of influence that is really caused by discrimination from all correlated influences. In our approach, we make use of the causal network to capture the causal structure of thedoi:10.24963/ijcai.2017/549 dblp:conf/ijcai/ZhangWW17 fatcat:uuxtdile5rd6fnifadnmcqcxju