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Reinforcement learning is an essential paradigm for solving sequential decision problems under uncertainty.
Estimating causal effects of treatments in randomized and nonrandomized studies
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Review of The Art of Causal Conjecture
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Susan A. Murphy · 2003
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James H. Stock and Francesco Trebbi · 2003
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Causal Models: How People Think about the World and Its Alternatives
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Pearl’s calculus of intervention is complete
Yimin Huang and Marco Valtorta · 2006
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Instrumental Variables: Application and Limitations
Edwin P. Martens, Wiebe R. Pestman, Anthonius de Boer, Svetlana V. Belitser, and Olaf H. Klungel · 2006
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What counterfactuals can be tested
Ilya Shpitser and Judea Pearl · 2007
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Nonlinear causal discovery with additive noise models
Patrik Hoyer, Dominik Janzing, Joris M Mooij, Jonas Peters, and Bernhard Schölkopf · 2008
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The Importance of Discovery in Children’s Causal Learning from Interventions
David Sobel and Jessica Sommerville · 2010
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Kernel-based conditional independence test and application in causal discovery
Kun Zhang, Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2011
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Intrinsic Motivation and Reinforcement Learning
Andrew G. Barto · 2013
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Sensitivity Analysis for Causal Inference under Unmeasured Confounding and Measurement Error Problems
Iván Díaz and Mark J. van der Laan · 2013
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(More) Efficient Reinforcement Learning via Posterior Sampling
Ian Osband, Daniel Russo, and Benjamin Van Roy · 2013
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Instrumental variable methods for causal inference
Michael Baiocchi, Jing Cheng, and Dylan S. Small · 2014
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Elias Bareinboim, Jin Tian, and Judea Pearl · 2014
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Sample Efficient Reinforcement Learning with Gaussian Processes
Robert Grande, Thomas Walsh, and Jonathan How · 2014
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Judea Pearl and Elias Bareinboim · 2014
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Bandits with Unobserved Confounders: A Causal Approach
Elias Bareinboim, Andrew Forney, and Judea Pearl · 2015
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A comprehensive survey on safe reinforcement learning
Javier Garcıa and Fernando Fernández · 2015
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Assaf Hallak, Dotan Di Castro, and Shie Mannor · 2015
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Causal Inference in Statistics, Social, and Biomedical Sciences
Guido W. Imbens and Donald B. Rubin · 2015
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Fairness Constraints: Mechanisms for Fair Classification, July 2015
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P. Gummadi · 2015
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Kun Zhang, Mingming Gong, and Bernhard Schölkopf · 2015
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Causal inference and the data-fusion problem
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OpenAI Gym, June 2016
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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RL 2 : Fast Reinforcement Learning via Slow Reinforcement Learning
Yan Duan, John Schulman, Xi Chen, Peter L. Bartlett, Ilya Sutskever, and Pieter Abbeel · 2016
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Causal Inference in Statistics: A Primer
Madelyn Glymour, Judea Pearl, and Nicholas P. Jewell · 2016
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Domain Adaptation with Conditional Transferable Components
Mingming Gong, Kun Zhang, Tongliang Liu, Dacheng Tao, Clark Glymour, and Bernhard Schölkopf · 2016
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Mimic-iii, a freely accessible critical care database
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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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Markov Decision Processes with Unobserved Confounders: A Causal Approach
Junzhe Zhang and Elias Bareinboim · 2016
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Constrained policy optimization
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Risk-constrained reinforcement learning with percentile risk criteria
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Deep Reinforcement Learning from Human Preferences
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Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
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Counterfactual fairness
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A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning
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Curiosity-driven Exploration by Self-supervised Prediction
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Interpreting and using CPDAGs with background knowledge
Emilija Perkovic, Markus Kalisch, and Marloes H. Maathuis · 2017
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Elements of Causal Inference: Foundations and Learning Algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
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Deep Reinforcement Learning for Sepsis Treatment, November 2017
Aniruddh Raghu, Matthieu Komorowski, Imran Ahmed, Leo Celi, Peter Szolovits, and Marzyeh Ghassemi · 2017
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Identifying Best Interventions through Online Importance Sampling
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Mastering the game of Go without human knowledge
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Distral: Robust multitask reinforcement learning
Yee Teh, Victor Bapst, Wojciech M. Czarnecki, John Quan, James Kirkpatrick, Raia Hadsell, Nicolas Heess, and Razvan Pascanu · 2017
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Domain randomization for transferring deep neural networks from simulation to the real world
Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel · 2017
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Transfer Learning in Multi-Armed Bandits: A Causal Approach
Junzhe Zhang and Elias Bareinboim · 2017
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Causal Reasoning from Meta-reinforcement learning
Ishita Dasgupta, Jane Wang, Silvia Chiappa, Jovana Mitrovic, Pedro Ortega, David Raposo, Edward Hughes, Peter Battaglia, Matthew Botvinick, and Zeb Kurth-Nelson · 2018
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Meta-Reinforcement Learning of Structured Exploration Strategies
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Hirid, a high time-resolution icu dataset (version 1.1. 1)
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Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning
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Deconfounding Reinforcement Learning in Observational Settings
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Identifying Causal Effects With Proxy Variables of an Unmeasured Confounder
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