Counterfactually Fair Prediction Using Multiple Causal Models
This work addresses fairness in machine learning predictions for decision-makers using expert models, but it is incremental as it builds on existing frameworks.
The paper tackles the problem of aggregating predictions from multiple causal models provided by different experts while ensuring counterfactual fairness, and demonstrates the approach on a toy case study.
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.