  We consider the problem of learning fair decision systems in complex
scenarios in which a sensitive attribute might affect the decision along both
fair and unfair pathways. We introduce a causal approach to disregard effects
along unfair pathways that simplifies and generalizes previous literature. Our
method corrects observations adversely affected by the sensitive attribute, and
uses these to form a decision. This avoids disregarding fair information, and
does not require an often intractable computation of the path-specific effect.
We leverage recent developments in deep learning and approximate inference to
achieve a solution that is widely applicable to complex, non-linear scenarios.
