Counterfactually Fair Prediction Using Multiple Causal Models
  In this paper we study the problem of making predictions using multiple
structural casual models defined by different agents, under the constraint that
the prediction satisfies the criterion of counterfactual fairness. Relying on
the frameworks of causality, fairness and opinion pooling, we build upon and
extend previous work focusing on the qualitative aggregation of causal Bayesian
networks and causal models. In order to complement previous qualitative
results, we devise a method based on Monte Carlo simulations. This method
enables a decision-maker to aggregate the outputs of the causal models provided
by different experts while guaranteeing the counterfactual fairness of the
result. We demonstrate our approach on a simple, yet illustrative, toy case
study.
