A Causality Inspired Framework for Model Interpretation

Author:

Wu Chenwang1ORCID,Wang Xiting2ORCID,Lian Defu1ORCID,Xie Xing2ORCID,Chen Enhong1ORCID

Affiliation:

1. University of Science and Technology of China, Hefei, China

2. Microsoft Research Asia, Beijing, China

Funder

the National Key R&D Program of China

the National Natural Science Foundation of China

Publisher

ACM

Reference57 articles.

1. Yossi Adi , Einat Kermany , Yonatan Belinkov , Ofer Lavi , and Yoav Goldberg . 2017 . Fine-grained analysis of sentence embeddings using auxiliary prediction tasks . In Proceedings of ICLR'17 . Yossi Adi, Einat Kermany, Yonatan Belinkov, Ofer Lavi, and Yoav Goldberg. 2017. Fine-grained analysis of sentence embeddings using auxiliary prediction tasks. In Proceedings of ICLR'17.

2. Aman Anand . 2020. Clickbait Dataset . ( 2020 ). https://www.kaggle.com/datasets/amananandrai/clickbait-dataset . Aman Anand. 2020. Clickbait Dataset. (2020). https://www.kaggle.com/datasets/amananandrai/clickbait-dataset.

3. Network Dissection: Quantifying Interpretability of Deep Visual Representations

4. Yoshua Bengio , Nicholas Léonard , and Aaron Courville . 2013. Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432 ( 2013 ). Yoshua Bengio, Nicholas Léonard, and Aaron Courville. 2013. Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432 (2013).

5. Differentiable causal discovery from interventional data;Brouillard Philippe;Advances in Neural Information Processing Systems,2020

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