  Recent work on fairness in machine learning has focused on various
statistical discrimination criteria and how they trade off. Most of these
criteria are observational: They depend only on the joint distribution of
predictor, protected attribute, features, and outcome. While convenient to work
with, observational criteria have severe inherent limitations that prevent them
from resolving matters of fairness conclusively.
  Going beyond observational criteria, we frame the problem of discrimination
based on protected attributes in the language of causal reasoning. This
viewpoint shifts attention from "What is the right fairness criterion?" to
"What do we want to assume about the causal data generating process?" Through
the lens of causality, we make several contributions. First, we crisply
articulate why and when observational criteria fail, thus formalizing what was
before a matter of opinion. Second, our approach exposes previously ignored
subtleties and why they are fundamental to the problem. Finally, we put forward
natural causal non-discrimination criteria and develop algorithms that satisfy
them.
