Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation

9 March 2020

Huan Liu

Papers citing "Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation"

35 / 85 papers shown

Title
Reinforced Causal Explainer for Graph Neural Networks Xiang Wang Y. Wu An Zhang Fuli Feng Xiangnan He Tat-Seng Chua CML 17 46 0 23 Apr 2022
Interpretation of Black Box NLP Models: A Survey Shivani Choudhary N. Chatterjee S. K. Saha FAtt 32 10 0 31 Mar 2022
Cycle-Consistent Counterfactuals by Latent Transformations Saeed Khorram Li Fuxin BDL 16 32 0 28 Mar 2022
Text Transformations in Contrastive Self-Supervised Learning: A Review Amrita Bhattacharjee Mansooreh Karami Huan Liu SSL 27 23 0 22 Mar 2022
Testing Granger Non-Causality in Panels with Cross-Sectional Dependencies Lenon Minorics Ali Caner Türkmen D. Kernert Patrick Bloebaum Laurent Callot Dominik Janzing 9 1 0 23 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
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI Meike Nauta Jan Trienes Shreyasi Pathak Elisa Nguyen Michelle Peters Yasmin Schmitt Jorg Schlotterer M. V. Keulen C. Seifert ELM XAI 26 395 0 20 Jan 2022
COIN: Counterfactual Image Generation for VQA Interpretation Zeyd Boukhers Timo Hartmann Jan Jurjens 13 7 0 10 Jan 2022
Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities Waddah Saeed C. Omlin XAI 36 414 0 11 Nov 2021
A survey of Bayesian Network structure learning N. K. Kitson Anthony C. Constantinou Zhi-gao Guo Yang Liu Kiattikun Chobtham CML 24 182 0 23 Sep 2021
Counterfactual Evaluation for Explainable AI Yingqiang Ge Shuchang Liu Zelong Li Shuyuan Xu Shijie Geng Yunqi Li Juntao Tan Fei Sun Yongfeng Zhang CML 35 13 0 05 Sep 2021
Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions Eike Petersen Yannik Potdevin Esfandiar Mohammadi Stephan Zidowitz Sabrina Breyer ... Sandra Henn Ludwig Pechmann M. Leucker P. Rostalski Christian Herzog FaML AILaw OOD 27 21 0 20 Jul 2021
Robust Counterfactual Explanations on Graph Neural Networks Mohit Bajaj Lingyang Chu Zihui Xue J. Pei Lanjun Wang P. C. Lam Yong Zhang OOD 35 96 0 08 Jul 2021
Explaining Time Series Predictions with Dynamic Masks Jonathan Crabbé M. Schaar FAtt AI4TS 23 80 0 09 Jun 2021
A Review on Explainability in Multimodal Deep Neural Nets Gargi Joshi Rahee Walambe K. Kotecha 23 137 0 17 May 2021
Causal Inference in medicine and in health policy, a summary Wenhao Zhang Ramin Ramezani A. Naeim CML OOD 17 6 0 10 May 2021
Causal Learning for Socially Responsible AI Lu Cheng Ahmadreza Mosallanezhad Paras Sheth Huan Liu 71 13 0 25 Apr 2021
Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyond Xuhong Li Haoyi Xiong Xingjian Li Xuanyu Wu Xiao Zhang Ji Liu Jiang Bian Dejing Dou AAML FaML XAI HAI 15 315 0 19 Mar 2021
Explainable Artificial Intelligence Approaches: A Survey Sheikh Rabiul Islam W. Eberle S. Ghafoor Mohiuddin Ahmed XAI 21 103 0 23 Jan 2021
Explainability of deep vision-based autonomous driving systems: Review and challenges Éloi Zablocki H. Ben-younes P. Pérez Matthieu Cord XAI 37 169 0 13 Jan 2021
Outcome-Explorer: A Causality Guided Interactive Visual Interface for Interpretable Algorithmic Decision Making Md. Naimul Hoque Klaus Mueller CML 51 30 0 03 Jan 2021
Socially Responsible AI Algorithms: Issues, Purposes, and Challenges Lu Cheng Kush R. Varshney Huan Liu FaML 22 145 0 01 Jan 2021
Comprehensible Counterfactual Explanation on Kolmogorov-Smirnov Test Zicun Cong Lingyang Chu Yu Yang J. Pei 19 0 0 01 Nov 2020
Shapley Flow: A Graph-based Approach to Interpreting Model Predictions Jiaxuan Wang Jenna Wiens Scott M. Lundberg FAtt 9 87 0 27 Oct 2020
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects Amir-Hossein Karimi Gilles Barthe Bernhard Schölkopf Isabel Valera FaML 14 172 0 08 Oct 2020
Causal Explanations of Image Misclassifications Yan Min Miles K. Bennett CML 11 0 0 28 Jun 2020
Generative causal explanations of black-box classifiers Matthew R. O’Shaughnessy Gregory H. Canal Marissa Connor Mark A. Davenport Christopher Rozell CML 25 73 0 24 Jun 2020
Time Series Forecasting With Deep Learning: A Survey Bryan Lim S. Zohren AI4TS AI4CE 31 1,184 0 28 Apr 2020
Adversarial Attacks and Defenses: An Interpretation Perspective Ninghao Liu Mengnan Du Ruocheng Guo Huan Liu Xia Hu AAML 26 8 0 23 Apr 2020
Explaining Visual Models by Causal Attribution Álvaro Parafita Jordi Vitrià CML FAtt 62 35 0 19 Sep 2019
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 323 4,203 0 23 Aug 2019
A Survey of Learning Causality with Data: Problems and Methods Ruocheng Guo Lu Cheng Jundong Li P. R. Hahn Huan Liu CML 32 168 0 25 Sep 2018
A causal framework for explaining the predictions of black-box sequence-to-sequence models David Alvarez-Melis Tommi Jaakkola CML 227 201 0 06 Jul 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 251 3,683 0 28 Feb 2017
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 207 2,082 0 24 Oct 2016