Pooling of Causal Models under Counterfactual Fairness via Causal Judgement Aggregation
This work addresses fairness in machine learning by combining expert models, but it is incremental as it builds on existing causal and fairness frameworks with limited empirical validation.
The paper tackles the problem of aggregating multiple probabilistic causal models from different experts while ensuring the aggregated model satisfies counterfactual fairness, proposing two algorithms that guarantee fairness and comparing them in a toy case study.
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.