01704nas a2200133   4500008004100000245007000041210006900111520127400180100001901454700002001473700001601493700002401509856003701533       2016                        eng d00aCausal Discrimination Discovery Through Propensity Score Analysis0 aCausal Discrimination Discovery Through Propensity Score Analysi3 aSocial discrimination is considered illegal and unethical in the modern world. Such discrimination is often implicit in observed decisions' datasets, and anti-discrimination organizations seek to discover cases of discrimination and to understand the reasons behind them. Previous work in this direction adopted simple observational data analysis; however, this can produce biased results due to the effect of confounding variables. In this paper, we propose a causal discrimination discovery and understanding approach based on propensity score analysis. The propensity score is an effective statistical tool for filtering out the effect of confounding variables. We employ propensity score weighting to balance the distribution of individuals from protected and unprotected groups w.r.t. the confounding variables. For each individual in the dataset, we quantify its causal discrimination or favoritism with a neighborhood-based measure calculated on the balanced distributions. Subsequently, the causal discrimination/favoritism patterns are understood by learning a regression tree. Our approach avoids common pitfalls in observational data analysis and make its results legally admissible. We demonstrate the results of our approach on two discrimination datasets.1 aQureshi, Bilal1 aKamiran, Faisal1 aKarim, Asim1 aRuggieri, Salvatore  uhttps://arxiv.org/abs/1608.03735