Counterfactually Fair Prediction Using Multiple Causal Models

  • Fabio Massimo ZennaroEmail author
  • Magdalena Ivanovska
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11450)


In this paper we study the problem of making predictions using multiple structural causal 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 agents while guaranteeing the counterfactual fairness of the result. We demonstrate our approach on a simple, yet illustrative, toy case study.


Causality Structural causal networks Fairness Counterfactual fairness Opinion pooling Judgement aggregation Monte Carlo sampling 


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of InformaticsUniversity of OsloOsloNorway

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