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We formulate a general framework for building structural causal models (SCMs) with deep learning components.
Structured output learning with conditional generative flows
You Lu and Bert Huang · 1905
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Causality and econometrics
Herman O. A. Wold · 1954
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Causal diagrams for epidemiologic research
Sander Greenland, Judea Pearl, and James M. Robins · 1999
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Ageing and the brain
Ruth Peters · 2006
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Causality: Models, Reasoning, and Inference
Judea Pearl · 2009
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A family of nonparametric density estimation algorithms
Esteban G. Tabak and Cristina V. Turner · 2013
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Auto-encoding variational Bayes
Diederik P. Kingma and Max Welling · 2014
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Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
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Learning structured output representation using deep conditional generative models
Kihyuk Sohn, Honglak Lee, and Xinchen Yan · 2015
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Deep convolutional inverse graphics network
Tejas D. Kulkarni, William F. Whitney, Pushmeet Kohli, and Josh Tenenbaum · 2015
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UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age
Cathie Sudlow, John Gallacher, Naomi Allen, Valerie Beral, Paul Burton, John Danesh, Paul Downey, Paul Elliott, Jane Green, Martin Landray, et al · 2015
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Autoencoding beyond pixels using a learned similarity metric
Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, and Ole Winther · 2016
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Composing graphical models with neural networks for structured representations and fast inference
Matthew J. Johnson, David K. Duvenaud, Alex Wiltschko, Ryan P. Adams, and Sandeep R. Datta · 2016
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InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
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Image style transfer using convolutional neural networks
Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge · 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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Jason Hartford, Greg Lewis, Kevin Leyton-Brown, and Matt Taddy · 2017
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The Concrete distribution: A continuous relaxation of discrete random variables
Chris J. Maddison, Andriy Mnih, and Yee Whye Teh · 2017
Graphical generative adversarial networks
Chongxuan Li, Max Welling, Jun Zhu, and Bo Zhang · 2018
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Implicit causal models for genome-wide association studies
Dustin Tran and David M. Blei · 2018
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Causality for machine learning
Bernhard Schölkopf · 2019
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Explaining visual models by causal attribution
Álvaro Parafita Martínez and Jordi Vitrià Marca · 2019
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Preventing failures due to dataset shift: Learning predictive models that transport
Adarsh Subbaswamy, Peter Schulam, and Suchi Saria · 2019
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The seven tools of causal inference, with reflections on machine learning
Judea Pearl · 2019
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Conditional density estimation with Bayesian normalising flows
Brian L. Trippe and Richard E. Turner · 2017
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Adversarial feature learning
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Adversarially learned inference
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β \beta -VAE: Learning basic visual concepts with a constrained variational framework
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Image-to-image translation with conditional adversarial networks
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Normalizing flows for probabilistic modeling and inference
George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan · 2019
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Learning likelihoods with conditional normalizing flows
Christina Winkler, Daniel Worrall, Emiel Hoogeboom, and Max Welling · 2019
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Learning to synthesise the ageing brain without longitudinal data
Tian Xia, Agisilaos Chartsias, Chengjia Wang, and Sotirios A. Tsaftaris · 2019
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Morpho-MNIST: Quantitative assessment and diagnostics for representation learning
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PyTorch: An imperative style, high-performance deep learning library
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Eli Bingham, Jonathan P. Chen, Martin Jankowiak, Fritz Obermeyer, Neeraj Pradhan, Theofanis Karaletsos, Rohit Singh, Paul Szerlip, Paul Horsfall, and Noah D. Goodman · 2019
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Neural spline flows
Conor Durkan, Artur Bekasov, Iain Murray, and George Papamakarios · 2019
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Counterfactual off-policy evaluation with Gumbel-Max structural causal models
Michael Oberst and David Sontag · 2019
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CausalVAE: Structured causal disentanglement in variational autoencoder
Mengyue Yang, Furui Liu, Zhitang Chen, Xinwei Shen, Jianye Hao, and Jun Wang · 2020
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Explanation by progressive exaggeration
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