Research map / Probabilistic and scientific modeling / Causal discovery
Structural Causal Bandits: Where to Intervene?
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| restrict arms to POMISs instead of intervening on all variables at once | Thompson sampling · Task 3 | cumulative regret: 2784.8 -> 91.4 | Helped |
| restrict arms to POMISs instead of all-at-once | Thompson sampling · Task 2 (IV) | cumulative regret: 272.1 -> 16.1 | Helped |
| restrict arms to POMISs instead of brute-force over all subsets | Thompson sampling · Task 3 | cumulative regret: 1469.0 -> 91.4 | Helped |
| restrict arms to POMISs instead of MISs (ablation) | Thompson sampling · Task 3 | cumulative regret: 472.4 -> 91.4 | Helped |
| restrict arms to POMISs instead of MISs (ablation) | Thompson sampling · Task 2 (IV) | cumulative regret: 21.4 -> 16.1 | Helped |
| restrict arms to POMISs instead of all-at-once in Markovian model | kl-UCB · Task 1 | cumulative regret: 12 -> 3.0 | Helped |
| use POMIS arm set instead of all-at-once | kl-UCB · Task 1 | rounds to 95% optimal arm probability: 66 -> 20 | Helped |
| use MIS instead of brute-force arm set at early stage (ablation) | bandit solver · Task 1 | optimal arm probability | Hurt |
Closest papers by meaning
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- Certified Policy Optimisation for Nested Causal Bandits via PAC-Bayes Risk (NeurIPS 2026 · similarity 0.90)
- Prediction-Intervention Games and Invariant Sets (NeurIPS 2026 · similarity 0.88)
- Adversarial Causal Bayesian Optimization (ICLR 2024 · similarity 0.88)
- Linear Causal Bandits: Unknown Graph and Soft Interventions (NeurIPS 2024 · similarity 0.88)
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