# Achieving Counterfactual Fairness for Causal Bandit

arXiv: https://arxiv.org/abs/2109.10458 · HTML: https://mlsanity.com/paper/2109.10458/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 40 references that are in the map.
- First posted on arXiv 21 Sep 2021.

## Builds on (most cited first, 21 of 21)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [Delayed Impact of Fair Machine Learning](https://mlsanity.com/paper/1803.04383/) (2018)
- [Path-Specific Counterfactual Fairness](https://mlsanity.com/paper/1802.08139/) (2018)
- [Fairness in Learning: Classic and Contextual Bandits](https://mlsanity.com/paper/1605.07139/) (2016)
- [Causal Bandits: Learning Good Interventions via Causal Inference](https://mlsanity.com/paper/1606.03203/) (2016)
- [Fairness in Reinforcement Learning](https://mlsanity.com/paper/1611.03071/) (2016)
- [A causal framework for discovering and removing direct and indirect discrimination](https://mlsanity.com/paper/1611.07509/) (2016)
- [Multisided Fairness for Recommendation](https://mlsanity.com/paper/1707.00093/) (2017)
- [Stochastic Bandits with Linear Constraints](https://mlsanity.com/paper/2006.10185/) (2020)
- [PC-Fairness: A Unified Framework for Measuring Causality-based Fairness](https://mlsanity.com/paper/1910.12586/) (2019)
- [Fairness Under Composition](https://mlsanity.com/paper/1806.06122/) (2018)
- [Identifying Best Interventions through Online Importance Sampling](https://mlsanity.com/paper/1701.02789/) (2017)
- [Causal Bandits with Unknown Graph Structure](https://mlsanity.com/paper/2106.02988/) (2021)
- [Exploring Author Gender in Book Rating and Recommendation](https://mlsanity.com/paper/1808.07586/) (2018)
- [Individual Fairness in Pipelines](https://mlsanity.com/paper/2004.05167/) (2020)
- [Fair Pipelines](https://mlsanity.com/paper/1707.00391/) (2017)
- [Achieving User-Side Fairness in Contextual Bandits](https://mlsanity.com/paper/2010.12102/) (2020)
- [The Price of Local Fairness in Multistage Selection](https://mlsanity.com/paper/1906.06613/) (2019)
- [An Algorithmic Framework to Control Bias in Bandit-based Personalization](https://mlsanity.com/paper/1802.08674/) (2018)
- [Metric-Free Individual Fairness with Cooperative Contextual Bandits](https://mlsanity.com/paper/2011.06738/) (2020)

## Built on by (most cited first, 15 of 15)

- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [Fairness in Recommendation: Foundations, Methods and Applications](https://mlsanity.com/paper/2205.13619/) (2022)
- [Causal Reinforcement Learning: A Survey](https://mlsanity.com/paper/2307.01452/) (2023)
- [Survey on Fair Reinforcement Learning: Theory and Practice](https://mlsanity.com/paper/2205.10032/) (2022)
- [Causal Fairness under Unobserved Confounding: A Neural Sensitivity Framework](https://mlsanity.com/paper/2311.18460/) (2023)
- [BanditQ: Fair Bandits with Guaranteed Rewards](https://mlsanity.com/paper/2304.05219/) (2023)
- [Revisiting Reciprocal Recommender Systems: Metrics, Formulation, and Method](https://mlsanity.com/paper/2408.09748/) (2024)
- [Counterfactual Effect Decomposition in Multi-Agent Sequential Decision Making](https://mlsanity.com/paper/2410.12539/) (2024)
- [Fairness under uncertainty in sequential decisions](https://mlsanity.com/paper/2604.21711/) (2026)
- [Pragmatic Fairness: Developing Policies with Outcome Disparity Control](https://mlsanity.com/paper/2301.12278/) (2023)
- [Enhancing Temporal Sensitivity of Large Language Model for Recommendation with Counterfactual Tuning](https://mlsanity.com/paper/2507.03047/) (2025)
- [Causality Enhancement for Cross-Domain Recommendation](https://mlsanity.com/paper/2510.14641/) (2025)
- [Group-Sensitive Offline Contextual Bandits](https://mlsanity.com/paper/2510.27123/) (2025)
- [A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning](https://mlsanity.com/paper/2608.08743/) (2026)
- [Minimax Optimal Regret for Causal Logistic Bandits with Counterfactual Fairness](https://mlsanity.com/paper/2610.01377/) (2026)

## Abstract (arXiv)

In online recommendation, customers arrive in a sequential and stochastic manner from an underlying distribution and the online decision model recommends a chosen item for each arriving individual based on some strategy. We study how to recommend an item at each step to maximize the expected reward while achieving user-side fairness for customers, i.e., customers who share similar profiles will receive a similar reward regardless of their sensitive attributes and items being recommended. By incorporating causal inference into bandits and adopting soft intervention to model the arm selection strategy, we first propose the d-separation based UCB algorithm (D-UCB) to explore the utilization of the d-separation set in reducing the amount of exploration needed to achieve low cumulative regret. Based on that, we then propose the fair causal bandit (F-UCB) for achieving the counterfactual individual fairness. Both theoretical analysis and empirical evaluation demonstrate effectiveness of our algorithms.