Identifying Best Fair Intervention
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 35 references that are in the map. First posted on arXiv 8 Nov 2021.
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16 of 35 references.
References · 16
- 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,312
- 2017Counterfactual Fairnesscited by 804
- 2014Certifying and removing disparate impactcited by 715
- 2018Improving fairness in machine learning systems: What do industry practitioners need?cited by 262
- 2016Fairness in Learning: Classic and Contextual Banditscited by 168
- 2017Fair Inference On Outcomescited by 144
- 2016Causal Bandits: Learning Good Interventions via Causal Inferencecited by 101
- 2016Fairness in Reinforcement Learningcited by 95
- 2019Achieving Fairness in the Stochastic Multi-armed Bandit Problemcited by 78
- 2019Combinatorial Sleeping Bandits with Fairness Constraintscited by 68
- 2018Online Learning with an Unknown Fairness Metriccited by 62
- 2018Learning Optimal Fair Policiescited by 49
4 more
- 2019Fair Contextual Multi-Armed Bandits: Theory and Experimentscited by 42
- 2017Identifying Best Interventions through Online Importance Samplingcited by 39
- 2019Group Retention when Using Machine Learning in Sequential Decision Making: the Interplay between User Dynamics and Fairnesscited by 38
- 2018Causal Bandits with Propagating Inferencecited by 23
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