Research map / Bandits and online learning / Contextual bandits and Thompson sampling
Causal and structured bandits
Exploit causal graphs and auxiliary feedback to learn good interventions.
61 accepted papers at ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026) · an approach in Contextual bandits and Thompson sampling, keyword cluster “contextual bandits · contexts · user”
State History Tried Preprints Who Export
Where it stands
Not enough benchmark data Fewer than four papers report the same benchmark, so there is no record to follow.
19 papers in 2023–24 and 14 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic shrank, from 12.8% to 10.5%.
How this is decided
A benchmark is tracked when at least four papers of this approach report their own method on the same dataset and metric in at least two different years. Its record is the best number reported up to 2024 compared with the best reported in 2025–26: still improving means at least 1% better, flat means at most 0.2% better with at least two papers from 2025–26 trying. Numbers on a different scale from the rest (a fraction among percentages) and far outliers are left out. Results come from different papers with different setups, so they are signals, not head-to-head comparisons. The share of papers is counted at ICML, NeurIPS, ICLR, CVPR and AAAI.
How it got here
Most cited paper of each year
- 2016 · Fairness in Learning: Classic and Contextual Bandits (NeurIPS 2016 · 191 citations)
- 2017 · Active Learning for Accurate Estimation of Linear Models (ICML 2017 · 2 citations)
- 2018 · Fighting Boredom in Recommender Systems with Linear Reinforcement Learning (NeurIPS 2018 · 20 citations)
- 2019 · Efficient Counterfactual Learning from Bandit Feedback (AAAI 2019 · 22 citations)
- 2020 · Doubly robust off-policy evaluation with shrinkage (ICML 2020 · 17 citations)
- 2021 · Learning from eXtreme Bandit Feedback (AAAI 2021 · 11 citations)
- 2022 · Adaptively Exploiting d-Separators with Causal Bandits (NeurIPS 2022 · 2 citations)
- 2023 · An Information-Theoretic Analysis of Nonstationary Bandit Learning (ICML 2023 · 1 citation)
- 2024 · Causal Bandits for Linear Structural Equation Models (NeurIPS 2024 · 5 citations)
- 2025 · Clustering Items through Bandit Feedback: Finding the Right Feature out of Many (ICML 2025 · 0 citations)
- 2026 · Neural Logistic Bandits (ICML 2026 · 0 citations)
Earliest papers here
- Fairness in Learning: Classic and Contextual Bandits (NeurIPS 2016 · 191 citations)
- Causal Bandits: Learning Good Interventions via Causal Inference (NeurIPS 2016 · 71 citations)
- Multiple-Play Bandits in the Position-Based Model (NeurIPS 2016 · 24 citations)
What has been tried
51 papers here report 324 measured changes (a component added, removed or swapped, compared with a baseline): helped 64%, no clear effect 8%, hurt 19%, mixed 8%. Papers mostly publish changes that helped, so "helped" is over-represented.
Recent preprints
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Who publishes on it
Institutions on the most papers of this approach since 2023 (of 33 papers; from OpenAlex and the papers' affiliations).
- Purdue University · 4 papers
- Google DeepMind · 2 papers
- Amazon · 2 papers
- Google · 2 papers
- Tel Aviv University · 2 papers
- University of Toronto · 2 papers
- National University of Singapore · 2 papers
- The Alan Turing Institute · 2 papers
- London School of Economics · 2 papers
- University of Pennsylvania · 2 papers
Use it
Download BibTeX of 40 key papersOpen the topic in the interactive map
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Related approaches
Same keyword cluster
- Oracle-efficient contextual bandits (168 papers)
- Linear and generalized linear bandits (88 papers)
- Practical contextual bandit extensions (77 papers)
- Ranking bandits (42 papers)
Elsewhere in the topic
- Thompson sampling analysis (84 papers)
- Gaussian process bandit regret (26 papers)
Benchmark numbers, ablations and baseline numbers are extracted by AI from each paper's full text and can be wrong; the paper is the authority. Data: results index of 2026-11-02, citation counts from OpenAlex (2026-10-09).