Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice

27 March 2021

Ankur Taly

Papers citing "Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice"

38 / 38 papers shown

Title
Learning Probabilities of Causation from Finite Population Data Ang Li Song Jiang Yizhou Sun Judea Pearl AI4CE 36 7 0 16 Oct 2022
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective Satyapriya Krishna Tessa Han Alex Gu Steven Wu S. Jabbari Himabindu Lakkaraju 228 190 0 03 Feb 2022
Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals Sainyam Galhotra Romila Pradhan Babak Salimi CML 48 106 0 22 Mar 2021
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges Cynthia Rudin Chaofan Chen Zhi Chen Haiyang Huang Lesia Semenova Chudi Zhong FaML AI4CE LRM 118 662 0 20 Mar 2021
Causal Sufficiency and Actual Causation Sander Beckers 13 37 0 03 Feb 2021
Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End R. Mothilal Divyat Mahajan Chenhao Tan Amit Sharma FAtt CML 37 99 0 10 Nov 2020
Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models Tom Heskes E. Sijben I. G. Bucur Tom Claassen FAtt TDI 97 151 0 03 Nov 2020
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects Amir-Hossein Karimi Gilles Barthe Bernhard Schölkopf Isabel Valera FaML 47 172 0 08 Oct 2020
Concept Bottleneck Models Pang Wei Koh Thao Nguyen Y. S. Tang Stephen Mussmann Emma Pierson Been Kim Percy Liang 85 801 0 09 Jul 2020
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach Amir-Hossein Karimi Julius von Kügelgen Bernhard Schölkopf Isabel Valera CML 94 179 0 11 Jun 2020
Causal Feature Learning for Utility-Maximizing Agents David Kinney David S. Watson 34 9 0 18 May 2020
Decision-theoretic foundations for statistical causality P. Dawid CML AI4CE 13 52 0 26 Apr 2020
Problems with Shapley-value-based explanations as feature importance measures Indra Elizabeth Kumar Suresh Venkatasubramanian C. Scheidegger Sorelle A. Friedler TDI FAtt 64 362 0 25 Feb 2020
On The Reasons Behind Decisions Adnan Darwiche Auguste Hirth FaML 37 146 0 21 Feb 2020
Algorithmic Recourse: from Counterfactual Explanations to Interventions Amir-Hossein Karimi Bernhard Schölkopf Isabel Valera CML 42 340 0 14 Feb 2020
Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approach Carlos Fernandez F. Provost Xintian Han CML 31 70 0 21 Jan 2020
Counterfactual Explanation Algorithms for Behavioral and Textual Data Yanou Ramon David Martens F. Provost Theodoros Evgeniou FAtt 78 88 0 04 Dec 2019
"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations Himabindu Lakkaraju Osbert Bastani 46 251 0 15 Nov 2019
The many Shapley values for model explanation Mukund Sundararajan A. Najmi TDI FAtt 48 628 0 22 Aug 2019
The What-If Tool: Interactive Probing of Machine Learning Models James Wexler Mahima Pushkarna Tolga Bolukbasi Martin Wattenberg F. Viégas Jimbo Wilson VLM 76 487 0 09 Jul 2019
Learning Representations by Humans, for Humans Sophie Hilgard Nir Rosenfeld M. Banaji Jack Cao David C. Parkes OCL HAI AI4CE 49 29 0 29 May 2019
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations R. Mothilal Amit Sharma Chenhao Tan CML 100 1,005 0 19 May 2019
Explaining individual predictions when features are dependent: More accurate approximations to Shapley values K. Aas Martin Jullum Anders Løland FAtt TDI 48 610 0 25 Mar 2019
Interpretable machine learning: definitions, methods, and applications W. James Murdoch Chandan Singh Karl Kumbier R. Abbasi-Asl Bin Yu XAI HAI 124 1,428 0 14 Jan 2019
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations Francesco Locatello Stefan Bauer Mario Lucic Gunnar Rätsch Sylvain Gelly Bernhard Schölkopf Olivier Bachem OOD 96 1,451 0 29 Nov 2018
Abduction-Based Explanations for Machine Learning Models Alexey Ignatiev Nina Narodytska Sasha Rubin FAtt 46 223 0 26 Nov 2018
Actionable Recourse in Linear Classification Berk Ustun Alexander Spangher Yang Liu FaML 84 545 0 18 Sep 2018
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives Amit Dhurandhar Pin-Yu Chen Ronny Luss Chun-Chen Tu Pai-Shun Ting Karthikeyan Shanmugam Payel Das FAtt 89 587 0 21 Feb 2018
Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections Xin Zhang Armando Solar-Lezama Rishabh Singh FAtt 88 63 0 21 Feb 2018
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) Been Kim Martin Wattenberg Justin Gilmer Carrie J. Cai James Wexler F. Viégas Rory Sayres FAtt 162 1,828 0 30 Nov 2017
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR Sandra Wachter Brent Mittelstadt Chris Russell MLAU 73 2,332 0 01 Nov 2017
Explanation in Artificial Intelligence: Insights from the Social Sciences Tim Miller XAI 217 4,229 0 22 Jun 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 538 21,613 0 22 May 2017
The Mythos of Model Interpretability Zachary Chase Lipton FaML 123 3,672 0 10 Jun 2016
Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model Benjamin Letham Cynthia Rudin Tyler H. McCormick D. Madigan FAtt 48 743 0 05 Nov 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 808 149,474 0 22 Dec 2014
General theory for interactions in sufficient cause models with dichotomous exposures T. VanderWeele Thomas S. Richardson 49 31 0 29 Jan 2013
Probabilities of Causation: Bounds and Identification Jin Tian Judea Pearl 71 213 0 16 Jan 2013