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In this paper we consider the problem of combining multiple probabilisticcausal models, provided by different experts, under the requirement that theaggregated model satisfy the criterion of counterfactual fairness. We buildupon the work on causal models and fairness in machine learning, and we expressthe problem of combining multiple models within the framework of opinionpooling. We propose two simple algorithms, grounded in the theory ofcounterfactual fairness and causal judgment aggregation, that are guaranteed togenerate aggregated probabilistic causal models respecting the criterion offairness, and we compare their behaviors on a toy case study.

Pooling of Causal Models under Counterfactual Fairness via Causal Judgement Aggregation