mlsanityData to 7 Oct 2026

Identifying Best Interventions through Online Importance Sampling

arXiv 1701.02789 ↗First posted 10 Jan 2017stat.ML cs.IT cs.LGcited by 39

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Abstract (arXiv)

Motivated by applications in computational advertising and systems biology, we consider the problem of identifying the best out of several possible soft interventions at a source node V in an acyclic causal directed graph, to maximize the expected value of a target node Y (located downstream of V). Our setting imposes a fixed total budget for sampling under various interventions, along with cost constraints on different types of interventions. We pose this as a best arm identification bandit problem with K arms where each arm is a soft intervention at V, and leverage the information leakage among the arms to provide the first gap dependent error and simple regret bounds for this problem. Our results are a significant improvement over the traditional best arm identification results. We empirically show that our algorithms outperform the state of the art in the Flow Cytometry data-set, and also apply our algorithm for model interpretation of the Inception-v3 deep net that classifies images.

Key facts. Cited by 39 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Jan 2017.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 8; 2022: 7; 2023: 8; 2024: 7; 2025: 3; 2026: 5.

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  1. 2019Causal Confusion in Imitation Learningcited by 286
  2. 2023A Survey on Causal Reinforcement Learningcited by 43
  3. 2021Causal Bandits with Unknown Graph Structurecited by 37
  4. 2022Active Learning for Optimal Intervention Design in Causal Modelscited by 30
  5. 2023Causal Reinforcement Learning: A Surveycited by 22
  6. 2022Causal Bandits for Linear Structural Equation Modelscited by 19
  7. 2022Adaptively Exploiting d-Separators with Causal Banditscited by 18
  8. 2022Combinatorial Causal Banditscited by 16
  9. 2021A Causal Bandit Approach to Learning Good Atomic Interventions in Presence of Unobserved Confounderscited by 15
  10. 2021Achieving Counterfactual Fairness for Causal Banditcited by 15
  11. 2021Bandit Algorithms for Precision Medicinecited by 12
  12. 2023Causal Bandits without Graph Learningcited by 10
27 more
  1. 2022Combinatorial Pure Exploration of Causal Banditscited by 9
  2. 2021Causal Markov Decision Processes: Learning Good Interventions Efficientlycited by 7
  3. 2023Combinatorial Causal Bandits without Graph Skeletoncited by 7
  4. 2024Causal Bandits with General Causal Models and Interventionscited by 7
  5. 2023Learning Good Interventions in Causal Graphs via Coveringcited by 5
  6. 2023Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learningcited by 5
  7. 2024Linear Causal Bandits: Unknown Graph and Soft Interventionscited by 5
  8. 2024Partial Structure Discovery is Sufficient for No-regret Learning in Causal Banditscited by 5
  9. 2022Linear Combinatorial Semi-Bandit with Causally Related Rewardscited by 4
  10. 2022Causal Deep Reinforcement Learning Using Observational Datacited by 3
  11. 2024Improved Bound for Robust Causal Bandits with Linear Modelscited by 2
  12. 2024Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction to Linear Bandits, and Limitations around Unknown Marginalscited by 2
  13. 2025Graph Learning Is Suboptimal in Causal Banditscited by 2
  14. 2021Intervention Efficient Algorithm for Two-Stage Causal MDPscited by 1
  15. 2023Non-stationary Delayed Combinatorial Semi-Bandit with Causally Related Rewardscited by 1
  16. 2024Why Online Reinforcement Learning is Causalcited by 1
  17. 2024Causal Contextual Bandits with Adaptive Contextcited by 1
  18. 2025Causal Bandit Over Unknown Graphs: Upper Confidence Bounds With Backdoor Adjustmentcited by 1
  19. 2025The Minimal Search Space for Conditional Causal Banditscited by 1
  20. 2021Bandits with Stochastic Experts: Constant Regret, Empirical Experts and Episodescited by 0
  21. 2021Identifying Best Fair Interventioncited by 0
  22. 2023Optimal Observation-Intervention Trade-Off in Optimisation Problems with Causal Structurecited by 0
  23. 2026Integrating Causal DAGs in Deep RL: Activating Minimal Markovian States with Multi-Order Exposurecited by 0
  24. 2026Non-Parametric Rehearsal Learning via Conditional Mean Embeddingscited by 0
  25. 2026Causal Intelligence for Constraint-Aware Intervention Design to Induce State Transitionscited by 0
  26. 2026Information-Directed Sampling for Causal Banditscited by 0
  27. 2026Minimax Optimal Regret for Causal Logistic Bandits with Counterfactual Fairnesscited by 0