Abstract In this paper, we investigate the problem of discovering both direct and indirect discrimination from the historical data, and removing the discriminatory e?ects 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 the data. Then we model direct and indirect discrimination as the path-specific e?ects, which accurately identify the two types of discrimination as the causal e?ects transmitted along di?erent paths in the network. Based on that, we propose an e?ective algorithm for discovering direct and indirect discrimination, as well as an algorithm for precisely removing both types of discrimination while retaining good data utility. Experiments using the real dataset show the e?ectiveness of our approaches.