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Causal discovery (CD) from time-varying data is important in neuroscience, medicine, and machine learning.
Causal discovery with reinforcement learning
Shengyu Zhu and Zhitang Chen · 1906
Earlier work this paper cites.
Investigating causal relations by econometric models and cross-spectral methods
Clive WJ Granger · 1969
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Causal inference in the social and behavioral sciences
Michael E Sobel · 1995
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Optimal structure identification with greedy search
David Maxwell Chickering · 2002
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New introduction to multiple time series analysis
Helmut Lütkepohl · 2005
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Inferring causality in brain images: a perturbation approach
Tomáš Paus · 2005
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Effects of face-to-face restorative justice on victims of crime in four randomized, controlled trials
Lawrence W Sherman, Heather Strang, Caroline Angel, Daniel Woods, Geoffrey C Barnes, Sarah Bennett, and Nova Inkpen · 2005
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A linear non-gaussian acyclic model for causal discovery
Shohei Shimizu, Patrik O Hoyer, Aapo Hyvärinen, Antti Kerminen, and Michael Jordan · 2006
Earlier work this paper cites.
Nonlinear causal discovery with additive noise models
Patrik Hoyer, Dominik Janzing, Joris M Mooij, Jonas Peters, and Bernhard Schölkopf · 2008
Earlier work this paper cites.
Grouped graphical granger modeling methods for temporal causal modeling
Aurelie C Lozano, Naoki Abe, Yan Liu, and Saharon Rosset · 2009
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Estimation of a structural vector autoregression model using non-gaussianity
Aapo Hyvärinen, Kun Zhang, Shohei Shimizu, and Patrik O Hoyer · 2010
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Causal inference
Judea Pearl · 2010
Earlier work this paper cites.
Towards a rigorous assessment of systems biology models: the dream3 challenges
Robert J Prill, Daniel Marbach, Julio Saez-Rodriguez, Peter K Sorger, Leonidas G Alexopoulos, Xiaowei Xue, Neil D Clarke, Gregoire Altan-Bonnet, and Gustavo Stolovitzky · 2010
Earlier work this paper cites.
Discovering graphical granger causality using the truncating lasso penalty
Ali Shojaie and George Michailidis · 2010
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Distinguishing causes from effects using nonlinear acyclic causal models
Kun Zhang and Aapo Hyvärinen · 2010
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On directed information theory and granger causality graphs
Pierre-Olivier Amblard and Olivier JJ Michel · 2011
Earlier work this paper cites.
Network modelling methods for fmri
Stephen M Smith, Karla L Miller, Gholamreza Salimi-Khorshidi, Matthew Webster, Christian F Beckmann, Thomas E Nichols, Joseph D Ramsey, and Mark W Woolrich · 2011
Earlier work this paper cites.
Transfer entropy—a model-free measure of effective connectivity for the neurosciences
Raul Vicente, Michael Wibral, Michael Lindner, and Gordon Pipa · 2011
Earlier work this paper cites.
API design for machine learning software: experiences from the scikit-learn project
Lars Buitinck, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa, Andreas Mueller, Olivier Grisel, Vlad Niculae, Peter Prettenhofer, Alexandre Gramfort, Jaques Grobler, Robert Layton, Jake VanderPlas, Arnaud Joly, Brian Holt, and Gaël Varoquaux · 2013
Earlier work this paper cites.
Causal inference in public health
Thomas A Glass, Steven N Goodman, Miguel A Hernán, and Jonathan M Samet · 2013
Earlier work this paper cites.
Causal inference on time series using restricted structural equation models
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2013
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Earlier work this paper cites.
Convolutional neural networks for sentence classification
Yoon Kim · 2014
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Using fluctuation analysis to establish causal relations between cellular events without experimental perturbation
Erik S Welf and Gaudenz Danuser · 2014
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Causal inference in statistics, social, and biomedical sciences
Guido W Imbens and Donald B Rubin · 2015
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Surpassing human-level face verification performance on lfw with gaussianface
Chaochao Lu and Xiaoou Tang · 2015
Cited alongside, same era.
Backshift: Learning causal cyclic graphs from unknown shift interventions
Dominik Rothenhäusler, Christina Heinze, Jonas Peters, and Nicolai Meinshausen · 2015
Cited alongside, same era.
Causal discovery with attention-based convolutional neural networks
Meike Nauta, Doina Bucur, and Christin Seifert · 2019
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Detecting and quantifying causal associations in large nonlinear time series datasets
Jakob Runge, Peer Nowack, Marlene Kretschmer, Seth Flaxman, and Dino Sejdinovic · 2019
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The bitter lesson
Richard Sutton · 2019
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Grandmaster level in starcraft ii using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, Wojciech M Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H Choi, Richard Powell, Timo Ewalds, Petko Georgiev, et al · 2019
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The blessings of multiple causes
Yixin Wang and David M Blei · 2019
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An image is worth 16x16 words: Transformers for image recognition at scale
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Samuel Dodge and Lina Karam · 2016
Cited alongside, same era.
Temporal convolutional networks: A unified approach to action segmentation
Colin Lea, Rene Vidal, Austin Reiter, and Gregory D Hager · 2016
Cited alongside, same era.
Methods for causal inference from gene perturbation experiments and validation
Nicolai Meinshausen, Alain Hauser, Joris M Mooij, Jonas Peters, Philip Versteeg, and Peter Bühlmann · 2016
Cited alongside, same era.
Ramprasaath R. Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell, Devi Parikh, and Dhruv Batra · 2016
Cited alongside, same era.
Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
Cited alongside, same era.
Causal generative neural networks
Olivier Goudet, Diviyan Kalainathan, Philippe Caillou, Isabelle Guyon, David Lopez-Paz, and Michèle Sebag · 2017
Cited alongside, same era.
Could a neuroscientist understand a microprocessor?
Eric Jonas and Konrad Paul Kording · 2017
Cited alongside, same era.
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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High-recall causal discovery for autocorrelated time series with latent confounders
Andreas Gerhardus and Jakob Runge · 2020
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Dynotears: Structure learning from time-series data
Roxana Pamfil, Nisara Sriwattanaworachai, Shaan Desai, Philip Pilgerstorfer, Konstantinos Georgatzis, Paul Beaumont, and Bryon Aragam · 2020
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Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets
Jakob Runge · 2020
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Discovering nonlinear relations with minimum predictive information regularization
Tailin Wu, Thomas Breuel, Michael Skuhersky, and Jan Kautz · 2020
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Causal inference in randomized clinical trials
Cheng Zheng, Ran Dai, Robert Peter Gale, and Mei-Jie Zhang · 2020
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Pytorch library for cam methods
Jacob Gildenblat and contributors · 2021
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Causal inference for time series analysis: Problems, methods and evaluation
Raha Moraffah, Paras Sheth, Mansooreh Karami, Anchit Bhattacharya, Qianru Wang, Anique Tahir, Adrienne Raglin, and Huan Liu · 2021
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Toward causal representation learning
Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, and Yoshua Bengio · 2021
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Neural granger causality
Alex Tank, Ian Covert, Nicholas Foti, Ali Shojaie, and Emily B Fox · 2021
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Rhino: Deep causal temporal relationship learning with history-dependent noise
Wenbo Gong, Joel Jennings, Cheng Zhang, and Nick Pawlowski · 2022
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Learning to induce causal structure
Nan Rosemary Ke, Silvia Chiappa, Jane Wang, Jorg Bornschein, Theophane Weber, Anirudh Goyal, Matthew Botvinic, Michael Mozer, and Danilo Jimenez Rezende · 2022
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Amortized causal discovery: Learning to infer causal graphs from time-series data
Sindy Löwe, David Madras, Richard Zemel, and Max Welling · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Large-scale chemical process causal discovery from big data with transformer-based deep learning
Xiaotian Bi, Deyang Wu, Daoxiong Xie, Huawei Ye, and Jinsong Zhao · 2023
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