Identifying Best Fair Intervention

TL;DR

This paper introduces a method for selecting the optimal intervention in a causal model that maximizes outcomes while ensuring fairness, with theoretical guarantees and empirical validation.

Contribution

It proposes a novel approach for fair intervention selection in causal models, combining counterfactual estimation with theoretical error bounds.

Findings

The algorithm effectively identifies fair interventions in causal models.

Theoretical guarantees on error probability are established.

Empirical results demonstrate improved fairness and outcome maximization.

Abstract

We study the problem of best arm identification with a fairness constraint in a given causal model. The goal is to find a soft intervention on a given node to maximize the outcome while meeting a fairness constraint by counterfactual estimation with only partial knowledge of the causal model. The problem is motivated by ensuring fairness on an online marketplace. We provide theoretical guarantees on the probability of error and empirically examine the effectiveness of our algorithm with a two-stage baseline.