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Learning good interventions in a causal graph can be modelled as a stochastic multi-armed bandit problem with side-information.
Some aspects of the sequential design of experiments
Herbert Robbins · 1952
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Causality: Models, Reasoning, and Inference
Judea Pearl · 2000
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Finite-time analysis of the multiarmed bandit problem
Peter Auer, Nicolò Cesa-Bianchi, and Paul Fischer · 2002
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A general identification condition for causal effects
Jin Tian and Judea Pearl · 2002
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Causality
Judea Pearl · 2009
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Contextual multi-armed bandits
Tyler Lu, Dávid Pál, and Martin Pál · 2010
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Epsilon-first policies for budget-limited multi-armed bandits
Long Tran-Thanh, Archie C. Chapman, Enrique Munoz de Cote, Alex Rogers, and Nicholas R. Jennings · 2010
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Knapsack based optimal policies for budget-limited multi-armed bandits
Long Tran-Thanh, Archie Chapman, Alex Rogers, and Nicholas R Jennings · 2012
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Online learning with feedback graphs: Beyond bandits
Noga Alon, Nicolo Cesa-Bianchi, Ofer Dekel, and Tomer Koren · 2015
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Bandits with unobserved confounders: A causal approach
Elias Bareinboim, Andrew Forney, and Judea Pearl · 2015
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Causal bandits: Learning good interventions via causal inference
Finnian Lattimore, Tor Lattimore, and Mark D. Reid · 2016
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Cost-optimal learning of causal graphs
Murat Kocaoglu, Alex Dimakis, and Sriram Vishwanath · 2017
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Experimental design for learning causal graphs with latent variables
Murat Kocaoglu, Karthikeyan Shanmugam, and Elias Bareinboim · 2017
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Online learning for causal bandits
Vin Sachidananda and E Brunskill · 2017
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Structural causal bandits: Where to intervene?
Sanghack Lee and Elias Bareinboim · 2018
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Experimental design for cost-aware learning of causal graphs
Erik M. Lindgren, Murat Kocaoglu, Alexandros G. Dimakis, and Sriram Vishwanath · 2018
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Causal bandits with propagating inference
Akihiro Yabe, Daisuke Hatano, Hanna Sumita, Shinji Ito, Naonori Kakimura, Takuro Fukunaga, and Ken-ichi Kawarabayashi · 2018
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Structural causal bandits with non-manipulable variables
Sanghack Lee and Elias Bareinboim · 2019
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Efficient intervention design for causal discovery with latents
Raghavendra Addanki, Shiva Prasad Kasiviswanathan, Andrew McGregor, and Cameron Musco · 2020
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Efficiently learning and sampling interventional distributions from observations
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Identifying best interventions through online importance sampling
Rajat Sen, Karthikeyan Shanmugam, Alexandros G. Dimakis, and Sanjay Shakkottai · 2017
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Contextual bandits with latent confounders: An NMF approach
Rajat Sen, Karthikeyan Shanmugam, Murat Kocaoglu, Alexandros G. Dimakis, and Sanjay Shakkottai · 2017
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Arnab Bhattacharyya, Sutanu Gayen, Saravanan Kandasamy, Ashwin Maran, and N. V. Vinodchandran · 2020
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Regret analysis of bandit problems with causal background knowledge
Yangyi Lu, Amirhossein Meisami, Ambuj Tewari, and William Yan · 2020
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