  Discrimination discovery from data is an important task aiming at identifying
patterns of illegal and unethical discriminatory activities against
protected-by-law groups, e.g., ethnic minorities. While any legally-valid proof
of discrimination requires evidence of causality, the state-of-the-art methods
are essentially correlation-based, albeit, as it is well known, correlation
does not imply causation.
  In this paper we take a principled causal approach to the data mining problem
of discrimination detection in databases. Following Suppes' probabilistic
causation theory, we define a method to extract, from a dataset of historical
decision records, the causal structures existing among the attributes in the
data. The result is a type of constrained Bayesian network, which we dub
Suppes-Bayes Causal Network (SBCN). Next, we develop a toolkit of methods based
on random walks on top of the SBCN, addressing different anti-discrimination
legal concepts, such as direct and indirect discrimination, group and
individual discrimination, genuine requirement, and favoritism. Our experiments
on real-world datasets confirm the inferential power of our approach in all
these different tasks.
