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Causal reasoning has been an indispensable capability for humans and other intelligent animals to interact with the physical world.
Causality and econometrics
Arnold Zellner · 1979
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Causal reasoning in medicine: analysis of a protocol
Benjamin Kuipers and Jerome P Kassirer · 1984
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Causal understanding as a developmental primitive
Roberta Corrigan and Peggy Denton · 1996
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The causal effects of ideas on policies
Albert S Yee · 1996
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Categorization as causal reasoning
Bob Rehder · 2003
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Causal reasoning in rats
Aaron P Blaisdell, Kosuke Sawa, Kenneth J Leising, and Michael R Waldmann · 2006
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Explanation and understanding
Frank C. Keil · 2006
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A linear non-gaussian acyclic model for causal discovery
Shohei Shimizu, Patrik O Hoyer, Aapo Hyvärinen, and Antti Kerminen · 2006
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Serious fun: preschoolers engage in more exploratory play when evidence is confounded
Laura E Schulz and Elizabeth Baraff Bonawitz · 2007
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Do new caledonian crows solve physical problems through causal reasoning?
Alex H Taylor, Gavin R Hunt, Felipe S Medina, and Russell D Gray · 2008
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Nonlinear causal discovery with additive noise models
Patrik O Hoyer, Dominik Janzing, Joris M Mooij, Jonas Peters, and Bernhard Schölkopf · 2009
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Causality
Judea Pearl · 2009
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No-regret reductions for imitation learning and structured prediction
Stéphane Ross, Geoffrey J. Gordon, and J. Andrew Bagnell · 2010
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Visual causal feature learning
Krzysztof Chalupka, Pietro Perona, and Frederick Eberhardt · 2014
Cited alongside, same era.
Generalized thompson sampling for sequential decision-making and causal inference
Pedro A Ortega and Daniel A Braun · 2014
Cited alongside, same era.
Causal discovery with continuous additive noise models
Jonas Peters, Joris M Mooij, Dominik Janzing, and Bernhard Schölkopf · 2014
Cited alongside, same era.
Bandits with unobserved confounders: A causal approach
Elias Bareinboim, Andrew Forney, and Judea Pearl · 2015
Cited alongside, same era.
Causal phenotype discovery via deep networks
David C Kale, Zhengping Che, Mohammad Taha Bahadori, Wenzhe Li, Yan Liu, and Randall Wetzel · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
Sim-to-real transfer of robotic control with dynamics randomization
Xue Bin Peng, Marcin Andrychowicz, Wojciech Zaremba, and Pieter Abbeel · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Preparing for the unknown: Learning a universal policy with online system identification
Wenhao Yu, C. Karen Liu, and Greg Turk · 2017
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Woulda, coulda, shoulda: Counterfactually-guided policy search
Lars Buesing, Theophane Weber, Yori Zwols, Sebastien Racaniere, Arthur Guez, Jean-Baptiste Lespiau, and Nicolas Heess · 2018
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Human causal transfer: Challenges for deep reinforcement learning
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Cited alongside, same era.
Universal value function approximators
Tom Schaul, Daniel Horgan, Karol Gregor, and David Silver · 2015
Cited alongside, same era.
Towards adapting deep visuomotor representations from simulated to real environments
Eric Tzeng, Coline Devin, Judy Hoffman, Chelsea Finn, Xingchao Peng, Sergey Levine, Kate Saenko, and Trevor Darrell · 2015
Cited alongside, same era.
Causal inference and the data-fusion problem
Elias Bareinboim and Judea Pearl · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, Pieter Abbeel, and Wojciech Zaremba · 2017
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Cited alongside, same era.
M. Edmonds, J. Kubricht, C. Summers, Y. Zhu, B. Rothrock, S.C. Zhu, and H. Lu · 2018
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Stable baselines
Ashley Hill, Antonin Raffin, Maximilian Ernestus, Adam Gleave, Rene Traore, Prafulla Dhariwal, Christopher Hesse, Oleg Klimov, Alex Nichol, Matthias Plappert, Alec Radford, John Schulman, Szymon Sidor, and Yuhuai Wu · 2018
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Sam: Structural agnostic model, causal discovery and penalized adversarial learning
Diviyan Kalainathan, Olivier Goudet, Isabelle Guyon, David Lopez-Paz, and Michèle Sebag · 2018
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Learning plannable representations with Causal InfoGAN
Thanard Kurutach, Aviv Tamar, Ge Yang, Stuart J Russell, and Pieter Abbeel · 2018
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Meta reinforcement learning with latent variable gaussian processes
Steindór Sæmundsson, Katja Hofmann, and Marc Peter Deisenroth · 2018
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A meta-transfer objective for learning to disentangle causal mechanisms
Yoshua Bengio, Tristan Deleu, Nasim Rahaman, Rosemary Ke, Sébastien Lachapelle, Olexa Bilaniuk, Anirudh Goyal, and Christopher Pal · 2019
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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 · 2019
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Gradient-based neural dag learning
Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu, and Simon Lacoste-Julien · 2019
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Causal discovery with attention-based convolutional neural networks
Meike Nauta, Doina Bucur, and Christin Seifert · 2019
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Dag-gnn: Dag structure learning with graph neural networks
Yue Yu, Jie Chen, Tian Gao, and Mo Yu · 2019
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Environment probing interaction policies
Wenxuan Zhou, Lerrel Pinto, and Abhinav Gupta · 2019
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