Feature Attribution with Necessity and Sufficiency via Dual-stage
Perturbation Test for Causal Explanation

Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation

13 February 2024

Zhengting Huang

Zijian Li

Jose Miguel Hernandez-Lobato

Papers citing "Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation"

18 / 18 papers shown

Title
On the Probability of Necessity and Sufficiency of Explaining Graph Neural Networks: A Lower Bound Optimization Approach Ruichu Cai Yuxuan Zhu Xuexin Chen Yuan Fang Min-man Wu Jie Qiao Zhifeng Hao 78 7 0 31 Dec 2024
Invariant Learning via Probability of Sufficient and Necessary Causes Mengyue Yang Zhen Fang Yonggang Zhang Yali Du Furui Liu Jean-François Ton Jianhong Wang Jun Wang OODD 72 13 0 22 Sep 2023
Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning Juntao Tan Shijie Geng Zuohui Fu Yingqiang Ge Shuyuan Xu Yunqi Li Yongfeng Zhang 61 111 0 17 Feb 2022
Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond Anna Hedström Leander Weber Dilyara Bareeva Daniel G. Krakowczyk Franz Motzkus Wojciech Samek Sebastian Lapuschkin Marina M.-C. Höhne XAI ELM 40 175 0 14 Feb 2022
Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice David S. Watson Limor Gultchin Ankur Taly Luciano Floridi 50 64 0 27 Mar 2021
CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks Ana Lucic Maartje ter Hoeve Gabriele Tolomei Maarten de Rijke Fabrizio Silvestri 141 145 0 05 Feb 2021
Captum: A unified and generic model interpretability library for PyTorch Narine Kokhlikyan Vivek Miglani Miguel Martin Edward Wang B. Alsallakh ... Alexander Melnikov Natalia Kliushkina Carlos Araya Siqi Yan Orion Reblitz-Richardson FAtt 128 838 0 16 Sep 2020
GNNExplainer: Generating Explanations for Graph Neural Networks Rex Ying Dylan Bourgeois Jiaxuan You Marinka Zitnik J. Leskovec LLMAG 128 1,314 0 10 Mar 2019
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms Han Xiao Kashif Rasul Roland Vollgraf 254 8,856 0 25 Aug 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 975 21,815 0 22 May 2017
Interpretable Explanations of Black Boxes by Meaningful Perturbation Ruth C. Fong Andrea Vedaldi FAtt AAML 74 1,517 0 11 Apr 2017
Understanding Black-box Predictions via Influence Functions Pang Wei Koh Percy Liang TDI 169 2,878 0 14 Mar 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 175 5,968 0 04 Mar 2017
Semi-Supervised Classification with Graph Convolutional Networks Thomas Kipf Max Welling GNN SSL 585 28,999 0 09 Sep 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.0K 16,931 0 16 Feb 2016
Striving for Simplicity: The All Convolutional Net Jost Tobias Springenberg Alexey Dosovitskiy Thomas Brox Martin Riedmiller FAtt 232 4,665 0 21 Dec 2014
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 295 7,279 0 20 Dec 2013
Visualizing and Understanding Convolutional Networks Matthew D. Zeiler Rob Fergus FAtt SSL 530 15,861 0 12 Nov 2013