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Causal approaches to post-hoc explainability for black-box prediction models (e.g., deep neural networks trained on image pixel data) have become increasingly popular.
Aspects of scientific explanation
Carl G. Hempel · 1965
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Causal laws and effective strategies
Nancy Cartwright · 1979
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Scientific explanation and the causal structure of the world
Wesley C. Salmon · 1984
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Causation, prediction, and search
Peter L. Spirtes, Clark N. Glymour, and Richard Scheines · 2000
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Greedy function approximation: a gradient boosting machine
Jerome H Friedman · 2001
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Ancestral graph Markov models
Thomas S. Richardson and Peter Spirtes · 2002
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Making things happen: A theory of causal explanation
James Woodward · 2005
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Causality
Judea Pearl · 2009
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The Caltech-UCSD Birds-200-2011 Dataset
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A Bayesian approach to constraint based causal inference
Tom Claassen and Tom Heskes · 2012
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Learning high-dimensional directed acyclic graphs with latent and selection variables
Diego Colombo, Marloes H. Maathuis, Markus Kalisch, and Thomas S. Richardson · 2012
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Causal stability ranking
Daniel J. Stekhoven, Izabel Moraes, Gardar Sveinbjörnsson, Lars Hennig, Marloes H. Maathuis, and Peter Bühlmann · 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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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
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Order-independent constraint-based causal structure learning
Diego Colombo and Marloes H. Maathuis · 2014
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An unsupervised feature learning framework for basal cell carcinoma image analysis
John Arevalo, Angel Cruz-Roa, Viviana Arias, Eduardo Romero, and Fabio A González · 2015
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Visual causal feature learning
Krzysztof Chalupka, Pietro Perona, and Frederick Eberhardt · 2015
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The pascal visual object classes challenge: A retrospective
Mark Everingham, SM Ali Eslami, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2015
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Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation
Alex Goldstein, Adam Kapelner, Justin Bleich, and Emil Pitkin · 2015
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Constraint-based causal discovery from multiple interventions over overlapping variable sets
Sofia Triantafillou and Ioannis Tsamardinos · 2015
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Unsupervised discovery of el nino using causal feature learning on microlevel climate data
Krzysztof Chalupka, Tobias Bischoff, Pietro Perona, and Frederick Eberhardt · 2016
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Copula PC algorithm for causal discovery from mixed data
Ruifei Cui, Perry Groot, and Tom Heskes · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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A hybrid causal search algorithm for latent variable models
Juan Miguel Ogarrio, Peter Spirtes, and Joseph Ramsey · 2016
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‘Why should I trust you?’ Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Causal feature learning: an overview
Krzysztof Chalupka, Frederick Eberhardt, and Pietro Perona · 2017
A guide to deep learning in healthcare
Andre Esteva, Alexandre Robicquet, Bharath Ramsundar, Volodymyr Kuleshov, Mark DePristo, Katherine Chou, Claire Cui, Greg Corrado, Sebastian Thrun, and Jeff Dean · 2019
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Counterfactual visual explanations
Yash Goyal, Ziyan Wu, Jan Ernst, Dhruv Batra, Devi Parikh, and Stefan Lee · 2019
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CXPlain: Causal explanations for model interpretation under uncertainty
Patrick Schwab and Walter Karlen · 2019
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Shubham Sharma, Jette Henderson, and Joydeep Ghosh · 2019
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Association between surgical skin markings in dermoscopic images and diagnostic performance of a deep learning convolutional neural network for melanoma recognition
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Rita Georgina Guimaraes, Renata L. Rosa, Denise De Gaetano, Demostenes Z Rodriguez, and Graca Bressan · 2017
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Causal explanation
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Discovering causal signals in images
David Lopez-Paz, Robert Nishihara, Soumith Chintala, Bernhard Scholkopf, and Léon Bottou · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Elements of causal inference: foundations and learning algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
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Julia K. Winkler, Christine Fink, Ferdinand Toberer, Alexander Enk, Teresa Deinlein, Rainer Hofmann-Wellenhof, Luc Thomas, Aimilios Lallas, Andreas Blum, Wilhelm Stolz, and Holger A. Haenssle · 2019
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Debiasing concept-based explanations with causal analysis
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Google apologizes after its Vision AI produced racist results
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Problems with shapley-value-based explanations as feature importance measures
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Causal interpretability for machine learning-problems, methods and evaluation
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On completeness-aware concept-based explanations in deep neural networks
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Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases
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