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Causal Bandits: Learning Good Interventions via Causal Inference
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use causal-graph feedback (observe non-intervened variables) instead of ignoring it | parallel bandit algorithm vs Successive Reject · simulated parallel bandit, N=50, T=400, small m | simple regret | Helped |
| increase m(q) toward N (causal algorithms vs Successive Reject) | causal bandit algorithms · simulated parallel bandit, N=50, T=400 | simple regret | Hurt |
| use regression estimator (parallel algorithm) instead of truncated importance weighted estimator (general algorithm) (ablation) | causal bandit algorithms · simulated parallel bandit | simple regret | Helped |
| remove truncation of importance weights (larger B_a) (ablation) | general causal bandit algorithm · simulated parallel bandit | simple regret | Helped |
Closest papers by meaning
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- Additive Causal Bandits with Unknown Graph (ICML 2023 · similarity 0.91)
- Robustly Improving Bandit Algorithms with Confounded and Selection Biased Offline Data: A Causal Approach (AAAI 2024 · similarity 0.89)
- Partial Structure Discovery is Sufficient for No-regret Learning in Causal Bandits (NeurIPS 2024 · similarity 0.89)
- Linear Causal Bandits: Unknown Graph and Soft Interventions (NeurIPS 2024 · similarity 0.88)
- Multi-Armed Bandits with Interference: Bridging Causal Inference and Adversarial Bandits (ICML 2025 · similarity 0.87)
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