Research map / Bandits and online learning / Contextual bandits and Thompson sampling
Achieving Counterfactual Fairness for Causal Bandit
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Apply a counterfactual-fairness constraint to the policy search | F-UCB · Email Campaign | unfair decisions: 0 | Helped |
| Use a counterfactual-fairness constraint rather than unconstrained UCB | Email Campaign · Email Campaign, τ=0.1 | unfair decisions: 3030 vs 0 | Helped |
| Use d-separation information to estimate reward | D-UCB · Email Campaign | cumulative regret | Helped |
| Use causal information in the bandit algorithm | D-UCB, F-UCB, and C-UCB · Email Campaign | cumulative regret | Helped |
| Increase the fairness threshold from τ=0.1 to τ=0.5 | F-UCB · Email Campaign | cumulative regret: 392.12 -> 313.89 | Helped |
| Increase the fairness threshold | UCB, C-UCB, and D-UCB · Email Campaign | unfair decisions: 3030 -> 18; 3176 -> 27; 3473 -> 47 | Helped |
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