  Fairness-aware machine learning has attracted a surge of attention in many
domains, such as online advertising, personalized recommendation, and social
media analysis in web applications. Fairness-aware machine learning aims to
eliminate biases of learning models against certain subgroups described by
certain protected (sensitive) attributes such as race, gender, and age. Among
many existing fairness notions, counterfactual fairness is a popular notion
defined from a causal perspective. It measures the fairness of a predictor by
comparing the prediction of each individual in the original world and that in
the counterfactual worlds in which the value of the sensitive attribute is
modified. A prerequisite for existing methods to achieve counterfactual
fairness is the prior human knowledge of the causal model for the data.
However, in real-world scenarios, the underlying causal model is often unknown,
and acquiring such human knowledge could be very difficult. In these scenarios,
it is risky to directly trust the causal models obtained from information
sources with unknown reliability and even causal discovery methods, as
incorrect causal models can consequently bring biases to the predictor and lead
to unfair predictions. In this work, we address the problem of counterfactually
fair prediction from observational data without given causal models by
proposing a novel framework CLAIRE. Specifically, under certain general
assumptions, CLAIRE effectively mitigates the biases from the sensitive
attribute with a representation learning framework based on counterfactual data
augmentation and an invariant penalty. Experiments conducted on both synthetic
and real-world datasets validate the superiority of CLAIRE in both
counterfactual fairness and prediction performance.
