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Causal Machine Learning (CausalML) is an umbrella term for machine learning methods that formalize the data-generation process as a structural causal model (SCM).
Mart\’n Arjovsky, L\’eon Bottou, Ishaan Gulrajani and David Lopez-Paz · 1907
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Shalmali Joshi et al · 1907
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“Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View”
Deli Chen et al · 1909
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“Equalizing Recourse across Groups”
Vivek Gupta, Pegah Nokhiz, Chitradeep Roy and Suresh Venkatasubramanian · 1909
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“Strategic Classification is Causal Modeling in Disguise”
John Miller, Smitha Milli and Moritz Hardt · 1910
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“Learning Neural Causal Models from Unknown Interventions”
Nan Ke et al · 1910
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“Causality for Machine Learning”, 2019
Bernhard Sch\"olkopf · 1911
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“Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers”
Divyat Mahajan, Chenhao Tan and Amit Sharma · 1912
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“Tariff on animal and vegetable oils”
Philip Wright · 1928
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“Steps toward artificial intelligence”
Marvin Minsky · 1961
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“Information value theory”
Ronald Howard · 1966
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“Investigating causal relations by econometric models and cross-spectral methods”
Clive Granger · 1969
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“Estimating causal effects of treatments in randomized and nonrandomized studies.”
Donald Rubin · 1974
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“Complete Identification Methods for the Causal Hierarchy”
Ilya Shpitser and Judea Pearl · 1979
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“Evaluating influence diagrams”
Ross Shachter · 1986
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“Contrastive explanation”
Peter Lipton · 1990
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“Nonparametric bounds on treatment effects”
Charles Manski · 1990
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“Input Generalization in Delayed Reinforcement Learning: An Algorithm and Performance Comparisons.”
David Chapman and Leslie Kaelbling · 1991
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“Dyna, an integrated architecture for learning, planning, and reacting”
Richard Sutton · 1991
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“Ockham’s razor and Bayesian analysis”
William Jefferys and James Berger · 1992
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“Learning to Achieve Goals”
Leslie Kaelbling · 1993
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“Graphical Models”
Steffen Lauritzen · 1996
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“Separating Style and Content”
Joshua. Tenenbaum and William. Freeman · 1996
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“Reinforcement learning with selective perception and hidden state”
Andrew McCallum · 1996
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“Learning Bayesian Networks is NP-Complete”
David Chickering · 1996
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“Long Short-Term Memory”
Sepp Hochreiter and J\"urgen Schmidhuber · 1997
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“Efficient global optimization of expensive black-box functions”
Donald Jones, Matthias Schonlau and William Welch · 1998
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“A note about redundancy in influence diagrams”
Enrico Fagiuoli and Marco Zaffalon · 1998
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“Multiagent systems: a modern approach to distributed artificial intelligence”
Gerhard Weiss · 1999
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“Causation, prediction, and search”
Peter Spirtes, Clark Glymour, Richard Scheines and David Heckerman · 2000
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“The information bottleneck method”
Naftali Tishby, Fernando Pereira and William Bialek · 2000
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“The Pareto, Zipf and other power laws”
William Reed · 2001
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“Discovering Nonlinear Relations with Minimum Predictive Information Regularization”
Tailin Wu, Thomas Breuel, Michael Skuhersky and Jan Kautz · 2001
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“Topological and causal structure of the yeast transcriptional regulatory network”
Nabil Guelzim, Samuele Bottani, Paul Bourgine and Francois K\’ep\‘es · 2002
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“Influence diagrams for causal modelling and inference”
A Dawid · 2002
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“Autonomous helicopter flight via reinforcement learning.”
Andrew Ng et al · 2003
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“All learning is local: Multi-agent learning in global reward games”
Yu-Han Chang, Tracey Ho and Leslie Kaelbling · 2003
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“Partial identification of probability distributions”
Charles Manski · 2003
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“Retrospectives: Who invented instrumental variable regression?”
James Stock and Francesco Trebbi · 2003
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“Being Bayesian about network structure. A Bayesian approach to structure discovery in Bayesian networks”
Nir Friedman and Daphne Koller · 2003
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“Applied time series econometrics”
Helmut L\"utkepohl and Markus Kr\"atzig · 2004
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“Learning a similarity metric discriminatively, with application to face verification”
Sumit Chopra, Raia Hadsell and Yann LeCun · 2005
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“Causal protein-signaling networks derived from multiparameter single-cell data”
Karen Sachs et al · 2005
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“The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes”
Douwe Kiela et al · 2005
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“SAFER: A Structure-free Approach for Certified Robustness to Adversarial Word Substitutions”
Mao Ye, Chengyue Gong and Qiang Liu · 2005
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“Finding optimal Bayesian networks by dynamic programming”
Ajit Singh and Andrew Moore · 2005
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“A causal view of compositional zero-shot recognition”
Yuval Atzmon, Felix Kreuk, Uri Shalit and Gal Chechik · 2006
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“True to the Model or True to the Data?”
Hugh Chen, Joseph. Janizek, Scott Lundberg and Su-In Lee · 2006
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“Off-policy evaluation in infinite-horizon reinforcement learning with latent confounders”
Andrew Bennett, Nathan Kallus, Lihong Li and Ali Mousavi · 2007
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“Active learning of causal networks with intervention experiments and optimal designs”
Yang-Bo He and Zhi Geng · 2008
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“Collective classification in network data”
Prithviraj Sen et al · 2008
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“Causality”
Judea Pearl · 2009
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“Pure exploration in multi-armed bandits problems”
S\’ebastien Bubeck, R\’emi Munos and Gilles Stoltz · 2009
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“Causal Discovery for Causal Bandits utilizing Separating Sets”
Arnoud… de Kroon, Danielle Belgrave and Joris. Mooij · 2009
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“Uncovering Hidden Challenges in Query-Based Video Moment Retrieval”
Mayu Otani, Yuta Nakashima, Esa Rahtu and Janne Heikkil\"a · 2009
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“Learning Bayesian networks with the bnlearn R package”
Marco Scutari · 2009
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“Imagenet: A large-scale hierarchical image database”
Jia Deng et al · 2009
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“Estimating Individual Treatment Effects using Non-Parametric Regression Models: a Review”
Alberto Caron, Ioanna Manolopoulou and Gianluca Baio · 2009
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“Counterfactual Explanations for Machine Learning: A Review”
Sahil Verma, John. Dickerson and Keegan Hines · 2010
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“A survey of algorithmic recourse: definitions, formulations, solutions, and prospects”
Amir-Hossein Karimi, Gilles Barthe, Bernhard Sch\"olkopf and Isabel Valera · 2010
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“Best arm identification in multi-armed bandits.”
Jean-Yves Audibert, S\’ebastien Bubeck and R\’emi Munos · 2010
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“Brief Report: On the Consistency Rule in Causal Inference: "Axiom, Definition, Assumption, or Theorem?"”
Judea Pearl · 2010
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“Research design explained”, 2010
Mark L and Janina M · 2010
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“Identification, inference and sensitivity analysis for causal mediation effects”
Kosuke Imai, Luke Keele and Teppei Yamamoto · 2010
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“Causal discovery as a game”
Frederick Eberhardt · 2010
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“Counterfactual Fairness with Disentangled Causal Effect Variational Autoencoder”
Hyemi Kim et al · 2011
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“Capturing mental state reasoning with influence diagrams”
Alan Jern and Charles Kemp · 2011
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“Learning word vectors for sentiment analysis”
Andrew Maas et al · 2011
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“Invited commentary: understanding bias amplification”
Judea Pearl · 2011
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“Bayesian network learning with cutting planes”
James Cussens · 2011
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“Social equity: Its legacy, its promise”
Mary Guy and Sean McCandless · 2012
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“Best arm identification: A unified approach to fixed budget and fixed confidence”
Victor Gabillon, Mohammad Ghavamzadeh and Alessandro Lazaric · 2012
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“Efficient Bayes-adaptive reinforcement learning using sample-based search”
Arthur Guez, David Silver and Peter Dayan · 2012
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“On causal and anticausal learning”
Bernhard Sch\"olkopf et al · 2012
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“MuJoCo: A physics engine for model-based control”
Emanuel Todorov, Tom Erez and Yuval Tassa · 2012
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“Causal inference using graphical models with the R package pcalg”
Markus Kalisch et al · 2012
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“Quantifying causal influences”
Dominik Janzing, David Balduzzi, Moritz Grosse-Wentrup and Bernhard Sch\"olkopf · 2013
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“Auto-Encoding Variational Bayes”
Diederik Kingma and Max Welling · 2013
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“Deep inside convolutional networks: Visualising image classification models and saliency maps”
Karen Simonyan, Andrea Vedaldi and Andrew Zisserman · 2013
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“Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising.”
L\’eon Bottou et al · 2013
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“The Arcade Learning Environment: An Evaluation Platform for General Agents”
M.. Bellemare, Y. Naddaf, J. Veness and M. Bowling · 2013
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“Playing Atari with Deep Reinforcement Learning”
Volodymyr Mnih et al · 2013
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“Experiment selection for causal discovery”
Antti Hyttinen, Frederick Eberhardt and Patrik Hoyer · 2013
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“Generative adversarial nets”
Ian Goodfellow et al · 2014
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“On causal interpretation of race in regressions adjusting for confounding and mediating variables”
Tyler VanderWeele and Whitney Robinson · 2014
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“Race and Social Equity: A Nervous Area of Government”
Jasmine McGinnis · 2014
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“Superintelligence: Paths, Dangers, Strategies”
Nick Bostrom · 2014
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“Instrumental variable methods for causal inference”
Michael Baiocchi, Jing Cheng and Dylan Small · 2014
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“Deep manifold traversal: Changing labels with convolutional features”
Jacob Gardner et al · 2015
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“Deep Learning Face Attributes in the Wild”
Ziwei Liu, Ping Luo, Xiaogang Wang and Xiaoou Tang · 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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“Bandits with unobserved confounders: A causal approach”
Elias Bareinboim, Andrew Forney and Judea Pearl · 2015
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“Human-level control through deep reinforcement learning”
Volodymyr Mnih et al · 2015
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“Continuous control with deep reinforcement learning”
Timothy Lillicrap et al · 2015
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“Supervised learning for dynamical system learning”
Ahmed Hefny, Carlton Downey and Geoffrey Gordon · 2015
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“Inference of Intention and Permissibility in Moral Decision Making.”
Max Kleiman-Weiner, Tobias Gerstenberg, Sydney Levine and Joshua Tenenbaum · 2015
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“Contextual markov decision processes”
Assaf Hallak, Dotan Di and Shie Mannor · 2015
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“Learning Bayesian Networks with Thousands of Variables”
Mauro Scanagatta, Cassio de Campos, Giorgio Corani and Marco Zaffalon · 2015
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“Concave penalized estimation of sparse Gaussian Bayesian networks”
Bryon Aragam and Qing Zhou · 2015
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“Pixel recurrent neural networks”
Aaron Van, Nal Kalchbrenner and Koray Kavukcuoglu · 2016
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“Conditional Image Generation with PixelCNN Decoders”
A\"aron van Oord et al · 2016
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“Causal inference in statistics: A primer”
Madelyn Glymour, Judea Pearl and Nicholas Jewell · 2016
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“Domain Adaptation with Conditional Transferable Components”
Mingming Gong et al · 2016
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“Causal inference by using invariant prediction: identification and confidence intervals”
Jonas Peters, Peter B\"uhlmann and Nicolai Meinshausen · 2016
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“" Why should i trust you?" Explaining the predictions of any classifier”
Marco Ribeiro, Sameer Singh and Carlos Guestrin · 2016
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“Inherent trade-offs in the fair determination of risk scores”
Jon Kleinberg, Sendhil Mullainathan and Manish Raghavan · 2016
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“Causal Bandits: Learning Good Interventions via Causal Inference”
Finnian Lattimore, Tor Lattimore and Mark. Reid · 2016
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“Observational-Interventional Priors for Dose-Response Learning”
Ricardo Silva · 2016
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“Hidden parameter markov decision processes: A semiparametric regression approach for discovering latent task parametrizations”
Finale Doshi-Velez and George Konidaris · 2016
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“The AI alignment problem: why it is hard, and where to start”
Eliezer Yudkowsky · 2016
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“Markov decision processes with unobserved confounders: A causal approach”, 2016
Junzhe Zhang and Elias Bareinboim · 2016
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“Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings”
Tolga Bolukbasi et al · 2016
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“TensorFlow: a system for Large-Scale machine learning”
Mart\’n Abadi et al · 2016
Earlier work this paper cites.
“Mastering the game of Go with deep neural networks and tree search”
David Silver et al · 2016
Earlier work this paper cites.
“Ancestral Causal Inference”
Sara Magliacane, Tom Claassen and Joris. Mooij · 2016
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“Elements of Causal Inference - Foundations and Learning Algorithms”, Adaptive Computation and Machine Learning Series
J. Peters, D. Janzing and B. Sch\"olkopf · 2017
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“Zero-shot learning-the good, the bad and the ugly”
Yongqin Xian, Bernt Schiele and Zeynep Akata · 2017
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“Clevr: A diagnostic dataset for compositional language and elementary visual reasoning”
Justin Johnson et al · 2017
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“Attention Is All You Need”
Ashish Vaswani et al · 2017
Earlier work this paper cites.
“Causal feature learning: an overview”
Krzysztof Chalupka, Frederick Eberhardt and Pietro Perona · 2017
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“A unified approach to interpreting model predictions”
Scott Lundberg and Su-In Lee · 2017
Earlier work this paper cites.
“Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR”
Sandra Wachter, Brent. Mittelstadt and Chris Russell · 2017
Earlier work this paper cites.
“Counterfactual Fairness”
Matt. Kusner, Joshua. Loftus, Chris Russell and Ricardo Silva · 2017
Earlier work this paper cites.
“Avoiding discrimination through causal reasoning”
Niki Kilbertus et al · 2017
Earlier work this paper cites.
“Algorithmic decision making and the cost of fairness”
Sam Corbett-Davies et al · 2017
Earlier work this paper cites.
“Fair prediction with disparate impact: A study of bias in recidivism prediction instruments”
Alexandra Chouldechova · 2017
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“Reinforcement learning and causal models”
Samuel Gershman · 2017
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“Mastering chess and shogi by self-play with a general reinforcement learning algorithm”
David Silver et al · 2017
Earlier work this paper cites.
“Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics”, 2017
Ken Kansky et al · 2017
Earlier work this paper cites.
“Imitation learning: A survey of learning methods”
Ahmed Hussein, Mohamed Gaber, Eyad Elyan and Chrisina Jayne · 2017
Earlier work this paper cites.
“Prediction of early unplanned intensive care unit readmission in a UK tertiary care hospital: a cross-sectional machine learning approach”
Thomas Desautels et al · 2017
Earlier work this paper cites.
“Hindsight Experience Replay”
Marcin Andrychowicz et al · 2017
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“Safe model-based reinforcement learning with stability guarantees”
Felix Berkenkamp, Matteo Turchetta, Angela Schoellig and Andreas Krause · 2017
Earlier work this paper cites.
“Model-agnostic meta-learning for fast adaptation of deep networks”
Chelsea Finn, Pieter Abbeel and Sergey Levine · 2017
Earlier work this paper cites.
“HotFlip: White-Box Adversarial Examples for Text Classification”
Javid Ebrahimi, Anyi Rao, Daniel Lowd and Dejing Dou · 2017
Earlier work this paper cites.
“Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints”
Jieyu Zhao et al · 2017
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“Automatic differentiation in pytorch”, 2017
Adam Paszke et al · 2017
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“Mastering the game of Go without human knowledge”
David Silver et al · 2017
Earlier work this paper cites.
“Estimating individual treatment effect: generalization bounds and algorithms”
Uri Shalit, Fredrik Johansson and David Sontag · 2017
Earlier work this paper cites.
“Causal Effect Inference with Deep Latent-Variable Models”
Christos Louizos et al · 2017
Earlier work this paper cites.
“A million variables and more: the Fast Greedy Equivalence Search algorithm for learning high-dimensional graphical causal models, with an application to functional magnetic resonance images”
Joseph. Ramsey, Madelyn Glymour, Ruben Sanchez-Romero and Clark Glymour · 2017
Earlier work this paper cites.
“A study of problems encountered in Granger causality analysis from a neuroscience perspective”
Patrick. Stokes and Patrick. Purdon · 2017
Earlier work this paper cites.
“The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery.”
Zachary Lipton · 2018
Earlier work this paper cites.
“ML beyond Curve Fitting: An Intro to Causal Inference and do-Calculus”, 2018
Ferenc Husz\’ar · 2018
Earlier work this paper cites.
“Complete Graphical Characterization and Construction of Adjustment Sets in Markov Equivalence Classes of Ancestral Graphs”
Emilija Perkovi\’c, Johannes Textor, Markus Kalisch and Marloes. Maathuis · 2018
Earlier work this paper cites.
“Recognition in terra incognita”
Sara Beery, Grant Van and Pietro Perona · 2018
Earlier work this paper cites.
“Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination”
Zhirong Wu, Yuanjun Xiong, Stella Yu and Dahua Lin · 2018
Earlier work this paper cites.
“Learning Independent Causal Mechanisms”
Giambattista Parascandolo, Niki Kilbertus, Mateo Rojas-Carulla and Bernhard Sch\"olkopf · 2018
Earlier work this paper cites.
“Recasting Gradient-Based Meta-Learning as Hierarchical Bayes”
Erin Grant et al · 2018
Earlier work this paper cites.
“Large scale GAN training for high fidelity natural image synthesis”
Andrew Brock, Jeff Donahue and Karen Simonyan · 2018
Earlier work this paper cites.
“Data augmentation instead of explicit regularization”
Alex Hern\’andez-Garc\’a and Peter K\"onig · 2018
Earlier work this paper cites.
“Autoaugment: Learning augmentation policies from data”
Ekin Cubuk et al · 2018
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“Explainable artificial intelligence: A survey”
Filip Dosilovi\’c, Mario Brci\’c and Nikica Hlupi\’c · 2018
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“Sanity checks for saliency maps”
Julius Adebayo et al · 2018
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“Fair inference on outcomes”
Razieh Nabi and Ilya Shpitser · 2018
Earlier work this paper cites.
“Causal reasoning for algorithmic fairness”
Joshua Loftus, Chris Russell, Matt Kusner and Ricardo Silva · 2018
Earlier work this paper cites.
“Fairness in decision-making—the causal explanation formula”
Junzhe Zhang and Elias Bareinboim · 2018
Earlier work this paper cites.
“Eddie Murphy and the dangers of counterfactual causal thinking about detecting racial discrimination”
Issa Kohler-Hausmann · 2018
Earlier work this paper cites.
“Reinforcement Learning: An Introduction”
Richard. Sutton and Andrew. Barto · 2018
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“Causality from the Perspective of Reinforcement Learning”, 2018
Csaba Szepesvari · 2018
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“Double/debiased machine learning for treatment and structural parameters”
Victor Chernozhukov et al · 2018
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“Structural Causal Bandits: Where to Intervene?”
Sanghack Lee and Elias Bareinboim · 2018
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David Ha and J\"urgen Schmidhuber · 2018
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“Confounding-Robust Policy Improvement”
Nathan Kallus and Angela Zhou · 2018
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“Counterfactual Multi-Agent Policy Gradients”
Jakob. Foerster et al · 2018
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“Bidirectional conditional generative adversarial networks”
Ayush Jaiswal, Wael AbdAlmageed, Yue Wu and Premkumar Natarajan · 2018
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“AGI Safety Literature Review”
Tom Everitt, Gary Lea and Marcus Hutter · 2018
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“Optimization methods for large-scale machine learning”
L\’eon Bottou, Frank Curtis and Jorge Nocedal · 2018
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“The Blessings of Multiple Causes”
Yixin Wang and David. Blei · 2018
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“Generating Counterfactual Explanations with Natural Language”
Lisa Hendricks, Ronghang Hu, Trevor Darrell and Zeynep Akata · 2018
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“Counterfactual Fairness in Text Classification through Robustness”
Sahaj Garg et al · 2018
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“Generating Natural Language Adversarial Examples”
Moustafa Alzantot et al · 2018
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“Adversarial Example Generation with Syntactically Controlled Paraphrase Networks”
Mohit Iyyer, John Wieting, Kevin Gimpel and Luke Zettlemoyer · 2018
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“Universal Dependencies 2.3”, 2018
Joakim Nivre et al · 2018
Cited alongside, same era.
“Generalization in anti-causal learning”
N. Kilbertus*, G. Parascandolo* and B. Sch\"olkopf* · 2018
Cited alongside, same era.
“JAX: composable transformations of Python+NumPy programs”, 2018
James Bradbury et al · 2018
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“Meta-reinforcement learning of structured exploration strategies”
Abhishek Gupta et al · 2018
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“The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care”
Matthieu Komorowski et al · 2018
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“Masked Autoencoders Are Scalable Vision Learners”
Kaiming He et al · 2021
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“Human Trajectory Prediction via Counterfactual Analysis”
Guangyi Chen, Junlong Li, Jiwen Lu and Jie Zhou · 2021
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“Counterfactual VQA: A Cause-Effect Look at Language Bias”
Yulei Niu et al · 2021
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“Counterfactual attention learning for fine-grained visual categorization and re-identification”
Yongming Rao, Guangyi Chen, Jiwen Lu and Jie Zhou · 2021
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“Wilds: A benchmark of in-the-wild distribution shifts”
Pang Koh et al · 2021
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“The Risks of Invariant Risk Minimization”
Elan Rosenfeld, Pradeep Ravikumar and Andrej Risteski · 2021
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“A Survey of Methods for Explaining Black Box Models”
Riccardo Guidotti et al · 2018
Cited alongside, same era.
“Identifying causal effects with proxy variables of an unmeasured confounder”
Wang Miao, Zhi Geng and Eric Tchetgen · 2018
Cited alongside, same era.
“DAGs with NO TEARS: Continuous Optimization for Structure Learning”
Xun Zheng, Bryon Aragam, Pradeep Ravikumar and Eric. Xing · 2018
Cited alongside, same era.
“SATISFy: Towards a self-learning analyzer for time series forecasting in self-improving systems”
Christian Krupitzer, Martin Pfannem\"uller, Jean Kaddour and Christian Becker · 2018
Cited alongside, same era.
Alex Tank et al · 2018
Cited alongside, same era.
“Challenges of real-world reinforcement learning”
Gabriel Dulac-Arnold, Daniel Mankowitz and Todd Hester · 2019
Cited alongside, same era.
Later among the works it cites.
“Does invariant risk minimization capture invariance?”
Pritish Kamath, Akilesh Tangella, Danica Sutherland and Nathan Srebro · 2021
Later among the works it cites.
“Invariance principle meets information bottleneck for out-of-distribution generalization”
Kartik Ahuja et al · 2021
Later among the works it cites.
“Out-of-Distribution Generalization via Risk Extrapolation (REx)”
David Krueger et al · 2021
Later among the works it cites.
“Domain Generalization using Causal Matching”
Divyat Mahajan, Shruti Tople and Amit Sharma · 2021
Later among the works it cites.
“Recovering Latent Causal Factor for Generalization to Distributional Shifts”
Xinwei Sun et al · 2021
Later among the works it cites.
“Recurrent Independent Mechanisms”
Anirudh Goyal et al · 2021
Later among the works it cites.
“Fast And Slow Learning Of Recurrent Independent Mechanisms”
Kanika Madan et al · 2021
Later among the works it cites.
“Transporting Causal Mechanisms for Unsupervised Domain Adaptation”
Zhongqi Yue, Qianru Sun, Xian-Sheng Hua and Hanwang Zhang · 2021
Later among the works it cites.
“Counterfactual Generative Networks”
Axel Sauer and Andreas Geiger · 2021
Later among the works it cites.
“VACA: Design of Variational Graph Autoencoders for Interventional and Counterfactual Queries”
Pablo Sanchez-Martin, Miriam Rateike and Isabel Valera · 2021
Later among the works it cites.
“Diffusion models beat gans on image synthesis”
Prafulla Dhariwal and Alexander Nichol · 2021
Later among the works it cites.
“CausalVAE: Disentangled Representation Learning via Neural Structural Causal Models”
Mengyue Yang et al · 2021
Later among the works it cites.
“When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations”
Xiangning Chen, Cho-Jui Hsieh and Boqing Gong · 2021
Later among the works it cites.
“A survey on the explainability of supervised machine learning”
Nadia Burkart and Marco Huber · 2021
Later among the works it cites.
“Contrastive Explanations for Model Interpretability”
Alon Jacovi et al · 2021
Later among the works it cites.
“Counterfactual Explanations for Machine Learning: Challenges Revisited”
Sahil Verma, John Dickerson and Keegan Hines · 2021
Later among the works it cites.
“Algorithmic Recourse: from Counterfactual Explanations to Interventions”
Amir-Hossein Karimi, Bernhard Sch\"olkopf and Isabel Valera · 2021
Later among the works it cites.
“Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties”
Lisa Schut et al · 2021
Later among the works it cites.
“Meaningfully Explaining Model Mistakes Using Conceptual Counterfactuals”
Abubakar Abid, Mert Yuksekgonul and James Zou · 2021
Later among the works it cites.
“Algorithmic Recourse in Partially and Fully Confounded Settings Through Bounding Counterfactual Effects”
Julius von K\"ugelgen et al · 2021
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