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Pooling of Causal Models under Counterfactual Fairness via Causal Judgement Aggregation
[article]
2018
arXiv
pre-print
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
arXiv:1805.09866v2
fatcat:bizzwkipgrf3bpfhd5d7j25eei