Identifying Best Fair Intervention
This work addresses fairness in online marketplaces, offering a method for fair intervention selection, but it appears incremental as it builds on existing causal and fairness frameworks.
The paper tackles the problem of identifying the best intervention in a causal model under fairness constraints, aiming to maximize outcomes while ensuring fairness through counterfactual estimation with partial model knowledge. It provides theoretical error guarantees and shows empirical effectiveness compared to a two-stage baseline.
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.