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Judea Pearl · 2001
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Causal inference by choosing graphs with most plausible markov kernels
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James Bannon, Brad Windsor, Wenbo Song, and Tao Li · 2006
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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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A novel bayes model: Hidden naive bayes
Liangxiao Jiang, Harry Zhang, and Zhihua Cai · 2008
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Accelerating bayesian inference over nonlinear differential equations with gaussian processes
Ben Calderhead, Mark Girolami, and Neil Lawrence · 2008
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Probabilistic graphical models: principles and techniques
Daphne Koller and Nir Friedman · 2009
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Causality
Judea Pearl · 2009
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Intervention and causality: Forecasting traffic flows using a dynamic bayesian network
Catriona M. Queen and Casper J. Albers · 2009
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Directlingam: A direct method for learning a linear non-gaussian structural equation model
Shohei Shimizu, Takanori Inazumi, Yasuhiro Sogawa, Aapo Hyvarinen, Yoshinobu Kawahara, Takashi Washio, Patrik O Hoyer, Kenneth Bollen, and Patrik Hoyer · 2011
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Bayesian nonparametric modeling for causal inference
Jennifer L Hill · 2011
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On the identifiability of the post-nonlinear causal model
Kun Zhang and Aapo Hyvarinen · 2012
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Machine learning: a probabilistic perspective
Kevin P Murphy · 2012
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Dong Wang, Yuewei Yang, Chenyang Tao, Zhe Gan, Liqun Chen, Fanjie Kong, Ricardo Henao, and Lawrence Carin · 2012
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Improving tree augmented naive bayes for class probability estimation
Liangxiao Jiang, Zhihua Cai, Dianhong Wang, and Harry Zhang · 2012
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Single world intervention graphs (swigs): A unification of the counterfactual and graphical approaches to causality
Thomas S Richardson and James M Robins · 2013
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Independent component analysis: recent advances
Aapo Hyvärinen · 2013
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Strong completeness and faithfulness in bayesian networks
Christopher Meek · 2013
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Causal discovery with continuous additive noise models
Jonas Peters, Joris M. Mooij, Dominik Janzing, and Bernhard Schölkopf · 2014
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Estimating person-centered treatment (pet) effects using instrumental variables: an application to evaluating prostate cancer treatments
Anirban Basu · 2014
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Causal network inference using biochemical kinetics
Chris J. Oates, Frank Dondelinger, Nora Bayani, James Korkola, Joe W. Gray, and Sach Mukherjee · 2014
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Smoking and mortality — beyond established causes
Brian D. Carter, Christian C. Abnet, Diane Feskanich, Neal D. Freedman, Patricia Hartge, Cora E. Lewis, Judith K. Ockene, Ross L. Prentice, Frank E. Speizer, Michael J. Thun, and Eric J. Jacobs · 2015
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On the uniform convergence of relative frequencies of events to their probabilities
Vladimir N Vapnik and A Ya Chervonenkis · 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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Imputation approaches for potential outcomes in causal inference
Daniel Westreich, Jessie K Edwards, Stephen R Cole, Robert W Platt, Sunni L Mumford, and Enrique F Schisterman · 2015
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Estimating the causal impact of recommendation systems from observational data
Amit Sharma, Jake M Hofman, and Duncan J Watts · 2015
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Gradient matching methods for computational inference in mechanistic models for systems biology: A review and comparative analysis
Benn Macdonald and Dirk Husmeier · 2015
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Optimal rate of direct estimators in systems of ordinary differential equations linear in functions of the parameters
Itai Dattner and Chris AJ Klaassen · 2015
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 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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Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Deep feature weighting for naive bayes and its application to text classification
Liangxiao Jiang, Chaoqun Li, Shasha Wang, and Lungan Zhang · 2016
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Elements of causal inference: foundations and learning algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
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Estimating individual treatment effect: generalization bounds and algorithms
Uri Shalit, Fredrik D Johansson, and David Sontag · 2017
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Bayesian inference of individualized treatment effects using multi-task gaussian processes
Ahmed M Alaa and Mihaela Van Der Schaar · 2017
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Counterfactual fairness
Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva · 2017
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Déja vu: The importance of time and causality in recommender systems
Justin Basilico and Yves Raimond · 2017
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Introduction to the foundations of causal discovery
Frederick Eberhardt · 2017
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Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg, and Joris M Mooij · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Graphical modeling for multivariate hawkes processes with nonparametric link functions
Michael Eichler, Rainer Dahlhaus, and Johannes Dueck · 2017
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Learning large scale ordinary differential equation systems
Frederik Vissing Mikkelsen and Niels Richard Hansen · 2017
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Contextual Bandits with Latent Confounders: An NMF Approach
Rajat Sen, Karthikeyan Shanmugam, Murat Kocaoglu, Alex Dimakis, and Sanjay Shakkottai · 2017
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Causal generative neural networks
Olivier Goudet, Diviyan Kalainathan, Philippe Caillou, Isabelle Guyon, David Lopez-Paz, and Michèle Sebag · 2017
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Invariant representation learning for treatment effect estimation
Claudia Shi, Victor Veitch, and David M Blei · 2021
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Causal network models of sars-cov-2 expression and aging to identify candidates for drug repurposing
Anastasiya Belyaeva, Louis Cammarata, Adityanarayanan Radhakrishnan, Chandler Squires, Karren Dai Yang, GV Shivashankar, and Caroline Uhler · 2021
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Drug repurposing of metformin for alzheimer’s disease: Combining causal inference in medical records data and systems pharmacology for biomarker identification
Marie-Laure Charpignon, Bella Vakulenko-Lagun, Bang Zheng, Colin Magdamo, Bowen Su, Kyle Evans, Steve Rodriguez, Artem Sokolov, Sarah Boswell, Yi-Han Sheu, et al · 2021
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Evaluating the heterogeneous effect of extended incubation to blastocyst transfer on the implantation outcome via causal inference
Yoav Kan-Tor, Naama Srebnik, Matan Gavish, Uri Shalit, and Amnon Buxboim · 2021
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Learning causal representation for training cross-domain pose estimator via generative interventions
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The book of why: the new science of cause and effect
Judea Pearl and Dana Mackenzie · 2018
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Dags with no tears: Continuous optimization for structure learning
Xun Zheng, Bryon Aragam, Pradeep K Ravikumar, and Eric P Xing · 2018
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Forecasting treatment responses over time using recurrent marginal structural networks
Bryan Lim · 2018
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Ganite: Estimation of individualized treatment effects using generative adversarial nets
Jinsung Yoon, James Jordon, and Mihaela Van Der Schaar · 2018
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Causal reasoning for algorithmic fairness
Joshua R Loftus, Chris Russell, Matt J Kusner, and Ricardo Silva · 2018
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Invariant models for causal transfer learning
Mateo Rojas-Carulla, Bernhard Schölkopf, Richard Turner, and Jonas Peters · 2018
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Identifying causal structure in large-scale kinetic systems
Niklas Pfister, Stefan Bauer, and Jonas Peters · 2018
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Xiheng Zhang, Yongkang Wong, Xiaofei Wu, Juwei Lu, Mohan Kankanhalli, Xiangdong Li, and Weidong Geng · 2021
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Learning robust models using the principle of independent causal mechanisms
Jens Müller, Robert Schmier, Lynton Ardizzone, Carsten Rother, and Ullrich Köthe · 2021
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Path-specific causal fair prediction via auxiliary graph structure learning
Liuyi Yao, Yaliang Li, Bolin Ding, Jingren Zhou, Jinduo Liu, Mengdi Huai, and Jing Gao · 2021
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Slaps: Self-supervision improves structure learning for graph neural networks
Bahare Fatemi, Layla El Asri, and Seyed Mehran Kazemi · 2021
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Vicause: Simultaneous missing value imputation and causal discovery with groups
Pablo Morales-Alvarez, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Miltiadis Allamanis, and Cheng Zhang · 2021
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Incorporating causal graphical prior knowledge into predictive modeling via simple data augmentation
Takeshi Teshima and Masashi Sugiyama · 2021
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Budgeted and non-budgeted causal bandits
Vineet Nair, Vishakha Patil, and Gaurav Sinha · 2021
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Causal bandits with unknown graph structure
Yangyi Lu, Amirhossein Meisami, and Ambuj Tewari · 2021
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Dynamic causal bayesian optimization
Virginia Aglietti, Neil Dhir, Javier González, and Theodoros Damoulas · 2021
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Bandits with partially observable confounded data
Guy Tennenholtz, Uri Shalit, Shie Mannor, and Yonathan Efroni · 2021
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Provably efficient causal reinforcement learning with confounded observational data
Lingxiao Wang, Zhuoran Yang, and Zhaoran Wang · 2021
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Resolving causal confusion in reinforcement learning via robust exploration
Clare Lyle, Amy Zhang, Minqi Jiang, Joelle Pineau, and Yarin Gal · 2021
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Causal influence detection for improving efficiency in reinforcement learning
Maximilian Seitzer, Bernhard Schölkopf, and Georg Martius · 2021
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Causal inference q-network: Toward resilient reinforcement learning
Chao-Han Huck Yang, I Hung, Te Danny, Yi Ouyang, and Pin-Yu Chen · 2021
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Causal reinforcement learning using observational and interventional data
Maxime Gasse, Damien Grasset, Guillaume Gaudron, and Pierre-Yves Oudeyer · 2021
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Causal-tgan: Generating tabular data using causal generative adversarial networks
Bingyang Wen, Luis Oliveros Colon, KP Subbalakshmi, and Rajarathnam Chandramouli · 2021
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Counterfactual generative networks
Axel Sauer and Andreas Geiger · 2021
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Boosting synthetic data generation with effective nonlinear causal discovery
Martina Cinquini, Fosca Giannotti, and Riccardo Guidotti · 2021
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Generative interventions for causal learning
Chengzhi Mao, Augustine Cha, Amogh Gupta, Hao Wang, Junfeng Yang, and Carl Vondrick · 2021
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NeurIPS workshop on causal machine learning for real-world impact (CML4Impact), 2022
Nick Pawlowski, Jeroen Berrevoets, Caroline Uhler, Kun Zhang, Mihaela van der Schaar, and Cheng Zhang · 2022
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On pearl’s hierarchy and the foundations of causal inference
Elias Bareinboim, Juan D Correa, Duligur Ibeling, and Thomas Icard · 2022
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Causal models for dynamical systems
Jonas Peters, Stefan Bauer, and Niklas Pfister · 2022
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Continuous-time modeling of counterfactual outcomes using neural controlled differential equations
Nabeel Seedat, Fergus Imrie, Alexis Bellot, Zhaozhi Qian, and Mihaela van der Schaar · 2022
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Weakly supervised causal representation learning
Johann Brehmer, Pim De Haan, Phillip Lippe, and Taco S Cohen · 2022
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Causal representation learning for instantaneous and temporal effects in interactive systems
Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M Asano, Taco Cohen, and Efstratios Gavves · 2022
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Causal imputation via synthetic interventions
Chandler Squires, Dennis Shen, Anish Agarwal, Devavrat Shah, and Caroline Uhler · 2022
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Individual treatment effect estimation in the presence of unobserved confounding using proxies: a cohort study in stage iii non-small cell lung cancer
Wouter AC van Amsterdam, Joost JC Verhoeff, Netanja I Harlianto, Gijs A Bartholomeus, Aahlad Manas Puli, Pim A de Jong, Tim Leiner, Anne SR van Lindert, Marinus JC Eijkemans, and Rajesh Ranganath · 2022
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The impact of covid-19 on airfares—a machine learning counterfactual analysis
Florian Wozny · 2022
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Machine learning applications in drug repurposing
Fan Yang, Qi Zhang, Xiaokang Ji, Yanchun Zhang, Wentao Li, Shaoliang Peng, and Fuzhong Xue · 2022
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Causal inference in medical records and complementary systems pharmacology for metformin drug repurposing towards dementia
Marie-Laure Charpignon, Bella Vakulenko-Lagun, Bang Zheng, Colin Magdamo, Bowen Su, Kyle Evans, Steve Rodriguez, Artem Sokolov, Sarah Boswell, Yi-Han Sheu, et al · 2022
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On the opportunity of causal learning in recommendation systems: Foundation, estimation, prediction and challenges
Peng Wu, Haoxuan Li, Yuhao Deng, Wenjie Hu, Quanyu Dai, Zhenhua Dong, Jie Sun, Rui Zhang, and Xiao-Hua Zhou · 2022
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Simultaneous missing value imputation and structure learning with groups
Pablo Morales-Alvarez, Wenbo Gong, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Nick Pawlowski, Miltiadis Allamanis, and Cheng Zhang · 2022
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A large-scale observational study of the causal effects of a behavioral health nudge
Achille Nazaret and Guillermo Sapiro · 2022
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Tabpfn: A transformer that solves small tabular classification problems in a second
Noah Hollmann, Samuel Müller, Katharina Eggensperger, and Frank Hutter · 2022
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Causal discovery and injection for feed-forward neural networks
Fabrizio Russo and Francesca Toni · 2022
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Matching learned causal effects of neural networks with domain priors
Sai Srinivas Kancheti, Abbavaram Gowtham Reddy, Vineeth N Balasubramanian, and Amit Sharma · 2022
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Towards robust and adaptive motion forecasting: A causal representation perspective
Yuejiang Liu, Riccardo Cadei, Jonas Schweizer, Sherwin Bahmani, and Alexandre Alahi · 2022
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Incorporating causality in energy consumption forecasting using deep neural networks
Kshitij Sharma, Yogesh K Dwivedi, and Bhimaraya Metri · 2022
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A causal bandit approach to learning good atomic interventions in presence of unobserved confounders
Aurghya Maiti, Vineet Nair, and Gaurav Sinha · 2022
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Causal contextual bandits with targeted interventions
Chandrasekar Subramanian and Balaraman Ravindran · 2022
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Adaptively exploiting d-separators with causal bandits
Blair Bilodeau, Linbo Wang, and Daniel M. Roy · 2022
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Causal bandits without prior knowledge using separating sets
Arnoud De Kroon, Joris Mooij, and Danielle Belgrave · 2022
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A meta-reinforcement learning algorithm for causal discovery
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Causal discovery and reinforcement learning: A synergistic integration
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Reinforcement learning of causal variables using mediation analysis
Tue Herlau and Rasmus Larsen · 2022
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Causal counterfactuals for improving the robustness of reinforcement learning
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Causal deep reinforcement learning using observational data
Wenxuan Zhu, Chao Yu, and Qiang Zhang · 2022
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Dynamic causal effects evaluation in a/b testing with a reinforcement learning framework
Chengchun Shi, Xiaoyu Wang, Shikai Luo, Hongtu Zhu, Jieping Ye, and Rui Song · 2022
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Provably efficient causal model-based reinforcement learning for environment-agnostic generalization
Mirco Mutti, Riccardo De Santi, Emanuele Rossi, Juan Felipe Calderon, Michael M. Bronstein, and Marcello Restelli · 2022
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Weakly supervised disentangled generative causal representation learning
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Gflowcausal: Generative flow networks for causal discovery
Wenqian Li, Yinchuan Li, Shengyu Zhu, Yunfeng Shao, Jianye Hao, and Yan Pang · 2022
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Interventional causal representation learning
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ODE discovery for longitudinal heterogeneous treatment effects inference
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