  In this paper we consider the problem of combining multiple probabilistic
causal models, provided by different experts, under the requirement that the
aggregated model satisfy the criterion of counterfactual fairness. We build
upon the work on causal models and fairness in machine learning, and we express
the problem of combining multiple models within the framework of opinion
pooling. We propose two simple algorithms, grounded in the theory of
counterfactual fairness and causal judgment aggregation, that are guaranteed to
generate aggregated probabilistic causal models respecting the criterion of
fairness, and we compare their behaviors on a toy case study.
