Counterfactually Fair Prediction Using Multiple Causal Models

TL;DR

This paper introduces a Monte Carlo simulation-based method to aggregate multiple causal models from different agents, ensuring counterfactual fairness in predictions, and extends previous qualitative approaches with a quantitative technique.

Contribution

It develops a novel Monte Carlo simulation approach for aggregating causal models from multiple experts while guaranteeing counterfactual fairness in predictions.

Findings

Method guarantees counterfactual fairness in aggregated predictions.

Demonstrated approach on a toy case study.

Extends qualitative aggregation with quantitative Monte Carlo technique.

Abstract

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.