Causality-based Explanation of Classification Outcomes-Reference-Cited by-同舟云学术

Causality-based Explanation of Classification Outcomes

Published:2020-06-14 Issue: Volume: Page:
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Container-title:Proceedings of the Fourth International Workshop on Data Management for End-to-End Machine Learning
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Author:

Bertossi Leopoldo¹,Li Jordan²,Schleich Maximilian³,Suciu Dan³,Vagena Zografoula⁴

Affiliation:

1. Universidad Adolfo Ibáñez & RelationalAI Inc. and Millenium Institute for Foundational Research on Data (IMFD, Chile)

2. Carleton University and Ottawa, Canada

3. University of Washington & RelationalAI Inc.

4. RelationalAI Inc.

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3399579.3399865

Reference24 articles.

1. Leopoldo Bertossi Jordan Li Maximilian Schleich Dan Suciu and Zografoula Vagena. Causality-based explanation of classification outcomes. CoRR abs/2003.06868 2020. Leopoldo Bertossi Jordan Li Maximilian Schleich Dan Suciu and Zografoula Vagena. Causality-based explanation of classification outcomes. CoRR abs/2003.06868 2020.

2. From Causes for Database Queries to Repairs and Model-Based Diagnosis and Back

3. Chaofan Chen Kangcheng Lin Cynthia Rudin Yaron Shaposhnik Sijia Wang and Tong Wang. An interpretable model with globally consistent explanations for credit risk. CoRR abs/1811.12615 2018. Chaofan Chen Kangcheng Lin Cynthia Rudin Yaron Shaposhnik Sijia Wang and Tong Wang. An interpretable model with globally consistent explanations for credit risk. CoRR abs/1811.12615 2018.

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