Explaining Classifiers with Causal Concept Effect (CaCE)

16 July 2019

Papers citing "Explaining Classifiers with Causal Concept Effect (CaCE)"

43 / 43 papers shown

Title
GIFT: A Framework for Global Interpretable Faithful Textual Explanations of Vision Classifiers Éloi Zablocki Valentin Gerard Amaia Cardiel Eric Gaussier Matthieu Cord Eduardo Valle 79 0 0 23 Nov 2024
CausalConceptTS: Causal Attributions for Time Series Classification using High Fidelity Diffusion Models Juan Miguel Lopez Alcaraz Nils Strodthoff DiffM AI4TS CML 29 2 0 24 May 2024
Measuring Feature Dependency of Neural Networks by Collapsing Feature Dimensions in the Data Manifold Yinzhu Jin Matthew B. Dwyer P. T. Fletcher MedIm 23 0 0 18 Apr 2024
Implementing local-explainability in Gradient Boosting Trees: Feature Contribution Ángel Delgado-Panadero Beatriz Hernández-Lorca María Teresa García-Ordás J. Benítez-Andrades 32 52 0 14 Feb 2024
A Glitch in the Matrix? Locating and Detecting Language Model Grounding with Fakepedia Giovanni Monea Maxime Peyrard Martin Josifoski Vishrav Chaudhary Jason Eisner Emre Kiciman Hamid Palangi Barun Patra Robert West KELM 51 12 0 04 Dec 2023
Interpreting Pretrained Language Models via Concept Bottlenecks Zhen Tan Lu Cheng Song Wang Yuan Bo Jundong Li Huan Liu LRM 32 20 0 08 Nov 2023
Uncovering Unique Concept Vectors through Latent Space Decomposition Mara Graziani Laura Mahony An-phi Nguyen Henning Muller Vincent Andrearczyk 43 4 0 13 Jul 2023
Linking a predictive model to causal effect estimation Jiuyong Li Lin Liu Ziqi Xu Ha Xuan Tran T. Le Jixue Liu CML 26 0 0 10 Apr 2023
Towards Learning and Explaining Indirect Causal Effects in Neural Networks Abbaavaram Gowtham Reddy Saketh Bachu Harsh Nilesh Pathak Ben Godfrey V. Balasubramanian V. Varshaneya Satya Narayanan Kar CML 31 0 0 24 Mar 2023
CEnt: An Entropy-based Model-agnostic Explainability Framework to Contrast Classifiers' Decisions Julia El Zini Mohamad Mansour M. Awad 26 1 0 19 Jan 2023
Adapting to Latent Subgroup Shifts via Concepts and Proxies Ibrahim M. Alabdulmohsin Nicole Chiou Alexander DÁmour A. Gretton Sanmi Koyejo Matt J. Kusner Stephen R. Pfohl Olawale Salaudeen Jessica Schrouff Katherine Tsai 68 9 0 21 Dec 2022
Understanding and Enhancing Robustness of Concept-based Models Sanchit Sinha Mengdi Huai Jianhui Sun Aidong Zhang AAML 25 18 0 29 Nov 2022
Causal Proxy Models for Concept-Based Model Explanations Zhengxuan Wu Karel DÓosterlinck Atticus Geiger Amir Zur Christopher Potts MILM 77 35 0 28 Sep 2022
Explainable AI for clinical and remote health applications: a survey on tabular and time series data Flavio Di Martino Franca Delmastro AI4TS 28 91 0 14 Sep 2022
Unit Testing for Concepts in Neural Networks Charles Lovering Ellie Pavlick 25 28 0 28 Jul 2022
Spatial-temporal Concept based Explanation of 3D ConvNets Yi Ji Yu Wang K. Mori Jien Kato 3DPC FAtt 24 7 0 09 Jun 2022
Post-hoc Concept Bottleneck Models Mert Yuksekgonul Maggie Wang James Y. Zou 145 185 0 31 May 2022
Clinical outcome prediction under hypothetical interventions -- a representation learning framework for counterfactual reasoning Yikuan Li M. Mamouei Shishir Rao A. Hassaine D. Canoy Thomas Lukasiewicz K. Rahimi G. Salimi-Khorshidi OOD CML AI4CE 28 1 0 15 May 2022
Do Users Benefit From Interpretable Vision? A User Study, Baseline, And Dataset Leon Sixt M. Schuessler Oana-Iuliana Popescu Philipp Weiß Tim Landgraf FAtt 26 14 0 25 Apr 2022
ConceptExplainer: Interactive Explanation for Deep Neural Networks from a Concept Perspective Jinbin Huang Aditi Mishra Bum Chul Kwon Chris Bryan FAtt HAI 38 31 0 04 Apr 2022
Concept Evolution in Deep Learning Training: A Unified Interpretation Framework and Discoveries Haekyu Park Seongmin Lee Benjamin Hoover Austin P. Wright Omar Shaikh Rahul Duggal Nilaksh Das Kevin Li Judy Hoffman Duen Horng Chau 24 2 0 30 Mar 2022
Concept Embedding Analysis: A Review Gesina Schwalbe 32 28 0 25 Mar 2022
Human-Centered Concept Explanations for Neural Networks Chih-Kuan Yeh Been Kim Pradeep Ravikumar FAtt 34 25 0 25 Feb 2022
Evaluation Methods and Measures for Causal Learning Algorithms Lu Cheng Ruocheng Guo Raha Moraffah Paras Sheth K. S. Candan Huan Liu CML ELM 24 50 0 07 Feb 2022
Which Style Makes Me Attractive? Interpretable Control Discovery and Counterfactual Explanation on StyleGAN B. Li Qiulin Wang Jiquan Pei Yu Yang Xiangyang Ji CVBM 32 3 0 24 Jan 2022
A Causal Lens for Controllable Text Generation Zhiting Hu Erran L. Li 45 59 0 22 Jan 2022
On Causally Disentangled Representations Abbavaram Gowtham Reddy Benin Godfrey L V. Balasubramanian OOD CML 34 21 0 10 Dec 2021
Editing a classifier by rewriting its prediction rules Shibani Santurkar Dimitris Tsipras Mahalaxmi Elango David Bau Antonio Torralba A. Madry KELM 177 89 0 02 Dec 2021
Matching Learned Causal Effects of Neural Networks with Domain Priors Sai Srinivas Kancheti Abbavaram Gowtham Reddy V. Balasubramanian Amit Sharma CML 28 12 0 24 Nov 2021
Double Trouble: How to not explain a text classifier's decisions using counterfactuals synthesized by masked language models? Thang M. Pham Trung H. Bui Long Mai Anh Totti Nguyen 21 7 0 22 Oct 2021
Unsupervised Causal Binary Concepts Discovery with VAE for Black-box Model Explanation Thien Q. Tran Kazuto Fukuchi Youhei Akimoto Jun Sakuma CML 40 10 0 09 Sep 2021
Entropy-based Logic Explanations of Neural Networks Pietro Barbiero Gabriele Ciravegna Francesco Giannini Pietro Lió Marco Gori S. Melacci FAtt XAI 25 78 0 12 Jun 2021
Leveraging Sparse Linear Layers for Debuggable Deep Networks Eric Wong Shibani Santurkar A. Madry FAtt 22 88 0 11 May 2021
Rationalization through Concepts Diego Antognini Boi Faltings FAtt 24 19 0 11 May 2021
Explaining in Style: Training a GAN to explain a classifier in StyleSpace Oran Lang Yossi Gandelsman Michal Yarom Yoav Wald G. Elidan ... William T. Freeman Phillip Isola Amir Globerson Michal Irani Inbar Mosseri GAN 45 152 0 27 Apr 2021
Counterfactual Generative Networks Axel Sauer Andreas Geiger OOD BDL CML 41 123 0 15 Jan 2021
Unbox the Blackbox: Predict and Interpret YouTube Viewership Using Deep Learning Jiaheng Xie Xinyu Liu HAI 13 10 0 21 Dec 2020
Understanding Failures of Deep Networks via Robust Feature Extraction Sahil Singla Besmira Nushi S. Shah Ece Kamar Eric Horvitz FAtt 18 83 0 03 Dec 2020
Debiasing Concept-based Explanations with Causal Analysis M. T. Bahadori David Heckerman FAtt CML 11 38 0 22 Jul 2020
Generative causal explanations of black-box classifiers Matthew R. O’Shaughnessy Gregory H. Canal Marissa Connor Mark A. Davenport Christopher Rozell CML 30 73 0 24 Jun 2020
CausaLM: Causal Model Explanation Through Counterfactual Language Models Amir Feder Nadav Oved Uri Shalit Roi Reichart CML LRM 36 156 0 27 May 2020
Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation Raha Moraffah Mansooreh Karami Ruocheng Guo A. Raglin Huan Liu CML ELM XAI 27 213 0 09 Mar 2020
On Completeness-aware Concept-Based Explanations in Deep Neural Networks Chih-Kuan Yeh Been Kim Sercan Ö. Arik Chun-Liang Li Tomas Pfister Pradeep Ravikumar FAtt 122 297 0 17 Oct 2019