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The Causal Bandit is a variant of the classic Bandit problem where an agent must identify the best action in a sequential decision-making process, where the reward distribution of the actions displays a non-trivial dependence structure that is governed by a causal model.
On the likelihood that one unknown probability exceeds another in view of the evidence of two samples
William R Thompson · 1933
Earlier work this paper cites.
Asymptotically efficient adaptive allocation rules
Tze Leung Lai and Herbert Robbins · 1985
Earlier work this paper cites.
Causation, Prediction, and Search
Peter Spirtes, Clark Glymour, and Richard Scheines · 2000
Earlier work this paper cites.
Finite-time analysis of the multiarmed bandit problem
Peter Auer, Nicolo Cesa-Bianchi, and Paul Fischer · 2002
Earlier work this paper cites.
Learning Bayesian Networks
R.E. Neapolitan · 2004
Earlier work this paper cites.
Causal protein-signaling networks derived from multiparameter single-cell data
Karen Sachs, Omar Perez, Dana Pe’er, Douglas A Lauffenburger, and Garry P Nolan · 2005
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Causality
Judea Pearl · 2009
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Kullback–Leibler upper confidence bounds for optimal sequential allocation
Olivier Cappé, Aurélien Garivier, Odalric-Ambrym Maillard, Rémi Munos, Gilles Stoltz, et al · 2013
Cited alongside, same era.
Constraint-based causal discovery: Conflict resolution with answer set programming
Antti Hyttinen, Frederick Eberhardt, and Matti Järvisalo · 2014
Cited alongside, same era.
Causal bandits: Learning good interventions via causal inference
Finnian Lattimore, Tor Lattimore, and Mark D Reid · 2016
Cited alongside, same era.
Ancestral causal inference
Sara Magliacane, Tom Claassen, and Joris M. Mooij · 2016
Cited alongside, same era.
Causal inference by using invariant prediction: identification and confidence intervals
Jonas Peters, Peter Bühlmann, and Nicolai Meinshausen · 2016
Cited alongside, same era.
Identifying best interventions through online importance sampling
Domain adaptation by using causal inference to predict invariant conditional distributions
Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg, and Joris M Mooij · 2018
Later among the works it cites.
Invariant models for causal transfer learning
Mateo Rojas-Carulla, Bernhard Schölkopf, Richard Turner, and Jonas Peters · 2018
Later among the works it cites.
Causal bandits with propagating inference
Akihiro Yabe, Daisuke Hatano, Hanna Sumita, Shinji Ito, Naonori Kakimura, Takuro Fukunaga, and Ken-Ichi Kawarabayashi · 2018
Later among the works it cites.
Regret analysis of bandit problems with causal background knowledge
Yangyi Lu, Amirhossein Meisami, Ambuj Tewari, and William Yan · 2020
Closest in time.
Joint causal inference from multiple contexts
Joris M. Mooij, Sara Magliacane, and Tom Claassen · 2020
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Rajat Sen, Karthikeyan Shanmugam, Alexandros G Dimakis, and Sanjay Shakkottai · 2017
Cited alongside, same era.
Structural causal bandits: Where to intervene?
Sanghack Lee and Elias Bareinboim · 2018
Cited alongside, same era.
Philip Dawid · 2021
Closest in time.
Causal bandits with unknown graph structure
Yangyi Lu, Amirhossein Meisami, and Ambuj Tewari · 2021
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