  Recent years have seen much research on fairness in machine learning. Here,
mean difference (MD) or demographic parity is one of the most popular measures
of fairness. However, MD quantifies not only discrimination but also
explanatory bias which is the difference of outcomes justified by explanatory
features. In this paper, we devise novel models, called FairCEEs, which remove
discrimination while keeping explanatory bias. The models are based on
estimators of causal effect utilizing propensity score analysis. We prove that
FairCEEs with the squared loss theoretically outperform a naive MD constraint
model. We provide an efficient algorithm for solving FairCEEs in regression and
binary classification tasks. In our experiment on synthetic and real-world data
in these two tasks, FairCEEs outperformed an existing model that considers
explanatory bias in specific cases.
