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Discrimination discovery from data is an important task aiming at identifyingpatterns of illegal and unethical discriminatory activities againstprotected-by-law groups, e.g., ethnic minorities. While any legally-valid proofof discrimination requires evidence of causality, the state-of-the-art methodsare essentially correlation-based, albeit, as it is well known, correlationdoes not imply causation.  In this paper we take a principled causal approach to the data mining problemof discrimination detection in databases. Following Suppes' probabilisticcausation theory, we define a method to extract, from a dataset of historicaldecision records, the causal structures existing among the attributes in thedata. The result is a type of constrained Bayesian network, which we dubSuppes-Bayes Causal Network (SBCN). Next, we develop a toolkit of methods basedon random walks on top of the SBCN, addressing different anti-discriminationlegal concepts, such as direct and indirect discrimination, group andindividual discrimination, genuine requirement, and favoritism. Our experimentson real-world datasets confirm the inferential power of our approach in allthese different tasks.

Exposing the Probabilistic Causal Structure of Discrimination