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Necessity and sufficiency are the building blocks of all successful explanations.
Theory of Games and Economic Behavior
John von Neumann and Oskar Morgenstern · 1944
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A value for n -person games
Lloyd Shapley · 1953
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The magical number seven, plus or minus two: Some limits on our capacity for processing information
George A. Miller · 1955
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Causes and conditions
J.L. Mackie · 1965
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Causation
David Lewis · 1973
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Norm theory: Comparing reality to its alternatives
Daniel Kahneman and Dale T. Miller · 1986
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Contrastive explanation
Peter Lipton · 1990
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Adult income dataset, 1996
Ronny Kochavi and Barry Becker · 1996
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Causality: Models, Reasoning, and Inference
Judea Pearl · 2000
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Probabilities of causation: Bounds and identification
Jin Tian and Judea Pearl · 2000
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Testing Statistical Hypotheses
E.L. Lehmann and Joseph P. Romano · 2005
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Extremely randomized trees
Pierre Geurts, Damien Ernst, and Louis Wehenkel · 2006
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The optimal discovery procedure: A new approach to simultaneous significance testing
John D Storey · 2007
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Predictive learning via rule ensembles
Jerome H Friedman and Bogdan E Popescu · 2008
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Empirical and counterfactual conditions for sufficient cause interactions
Tyler J VanderWeele and James M Robins · 2008
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Natural language processing with Python: Analyzing text with the natural language toolkit
Steven Bird, Ewan Klein, and Edward Loper · 2009
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A survey of algorithmic recourse: Definitions, formulations, solutions, and prospects
Amir-Hossein Karimi, Gilles Barthe, Bernhard Schölkopf, and Isabel Valera · 2010
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Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts · 2011
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Towards unifying feature attribution and counterfactual explanations: Different means to the same end
Ramaravind K. Mothilal, Divyat Mahajan, Chenhao Tan, and Amit Sharma · 2011
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General theory for interactions in sufficient cause models with dichotomous exposures
Tyler J VanderWeele and Thomas S Richardson · 2012
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Contrastivism in Philosophy
Martijn Blaauw, editor · 2013
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GloVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning · 2014
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Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
Guido W Imbens and Donald B Rubin · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model
Benjamin Letham, Cynthia Rudin, Tyler H McCormick, and David Madigan · 2015
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Actual Causality
Joseph Y Halpern · 2016
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Tim Miller · 2019
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Assessing heuristic machine learning explanations with model counting
Nina Narodytska, Aditya Shrotri, Kuldeep S Meel, Alexey Ignatiev, and Joao Marques-Silva · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin · 2019
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The many Shapley values for model explanation
Mukund Sundararajan and Amir Najmi · 2019
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Actionable recourse in linear classification
Berk Ustun, Alexander Spangher, and Yang Liu · 2019
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The Hidden Assumptions behind Counterfactual Explanations and Principal Reasons
Solon Barocas, Andrew D Selbst, and Manish Raghavan · 2020
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Causal feature learning: an overview
Krzysztof Chalupka, Frederick Eberhardt, and Pietro Perona · 2017
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Explanations based on the missing: Towards contrastive explanations with pertinent negatives
Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Paishun Ting, Karthikeyan Shanmugam, and Payel Das · 2018
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The mythos of model interpretability
Zachary Lipton · 2018
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Counterfactual explanations without opening the black box: Automated decisions and the GDPR
Sandra Wachter, Brent Mittelstadt, and Chris Russell · 2018
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Explainable machine learning in deployment
Umang Bhatt, Alice Xiang, Shubham Sharma, Adrian Weller, Ankur Taly, Yunhan Jia, Joydeep Ghosh, Ruchir Puri, José M F Moura, and Peter Eckersley · 2020
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Explaining data-driven decisions made by AI systems: The counterfactual approach
C. Fernández-Loría, F. Provost, and X. Han · 2020
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Causal Shapley values: Exploiting causal knowledge to explain individual predictions of complex models
Tom Heskes, Evi Sijben, Ioan Gabriel Bucur, and Tom Claassen · 2020
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Problems with Shapley-value-based explanations as feature importance measures
Indra Kumar, Suresh Venkatasubramanian, Carlos Scheidegger, and Sorelle Friedler · 2020
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“How do I fool you?”: Manipulating user trust via misleading black box explanations
Himabindu Lakkaraju and Osbert Bastani · 2020
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The explanation game: Explaining machine learning models using shapley values
Luke Merrick and Ankur Taly · 2020
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A comparison of instance-level counterfactual explanation algorithms for behavioral and textual data: SEDC, LIME-C and SHAP-C
Yanou Ramon, David Martens, Foster Provost, and Theodoros Evgeniou · 2020
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LIMEtree: Interactively customisable explanations based on local surrogate multi-output regression trees
Kacper Sokol and Peter Flach · 2020
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The explanation game: a formal framework for interpretable machine learning
David S Watson and Luciano Floridi · 2020
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The what-if tool: Interactive probing of machine learning models
J. Wexler, M. Pushkarna, T. Bolukbasi, M. Wattenberg, F. Viégas, and J. Wilson · 2020
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Explaining black-box algorithms using probabilistic contrastive counterfactuals
Sainyam Galhotra, Romila Pradhan, and Babak Salimi · 2021
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Interpretable Machine Learning: A Guide for Making Black Box Models Interpretable
Christoph Molnar · 2021
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URL https://spamassassin.apache.org/old/publiccorpus/
Apache SpamAssassin, 2006 · 2021
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