Pooling of Causal Models under Counterfactual Fairness via Causal Judgement Aggregation

TL;DR

This paper introduces methods for aggregating multiple expert-provided causal models into a single fair model that satisfies counterfactual fairness, using opinion pooling and causal judgment aggregation techniques.

Contribution

It proposes two novel algorithms for combining probabilistic causal models under fairness constraints, ensuring the aggregated model remains counterfactually fair.

Findings

Algorithms guarantee fairness in aggregated models

Comparison on toy case study demonstrates effectiveness

Framework integrates causal judgment aggregation with fairness criteria

Abstract

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.