[33] Sungyong Seo, Jing Huang, Hao Yang, and Yan Liu. Interpretable convolutional neural networks with dual local and global attention for review rating prediction. In Proceedings of the eleventh ACM conference on recommender systems, pages 297-305, 2017.
[34] Peter Spirtes. Introduction to causal inference. Journal of Machine Learning Research, 11 (May):1643-1662, 2010.
[35] Peter Spirtes, Clark Glymour, and Richard Scheines. Causation, Prediction and Search. MIT Press, 2nd edition, 2000.
[36] Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In Proceedings of the 28th ACM international conference on information and knowledge management, pages 1441-1450, 2019.
[37] Xiaohai Sun, Dominik Janzing, Bernhard Scholkopf, and Kenji Fukumizu. A kernel-based causal learning algorithm. In Proceedings of the 24th international conference on Machine learning, pages 855-862, 2007.
[38] Khanh Hiep Tran, Azin Ghazimatin, and Rishiraj Saha Roy. Counterfactual explanations for neural recommenders. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 1627-1631, 2021.
[39] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017.
[40] Sandra Wachter, Brent Mittelstadt, and Chris Russell. Counterfactual explanations without opening the black box: Automated decisions and the gdpr. 2017. doi: 10.48550/ARXIV.1711.00399. URL https://arxiv.org/abs/1711.00399.
[41] Sarah Wiegrefe and Yuval Pinter. Attention is not not explanation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 11–20, 2019.
[42] Kevin Xia, Kai-Zhan Lee, Yoshua Bengio, and Elias Bareinboim. The causal-neural connection: Expressiveness, learnability, and inference. Advances in Neural Information Processing Systems, 34:10823-10836, 2021.
[43] Kevin Xia, Yushu Pan, and Elias Bareinboim. Causal models for counterfactual identification and estimation. In Proceedings of the 11th Eleventh International Conference on Learning Representations, forthcoming, 2023.
[44] Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio. Show, attend and tell: Neural image caption generation with visual attention. In International conference on machine learning, pages 2048-2057. PMLR, 2015.
[45] Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. XLnet: Generalized autoregressive pretraining for language understanding. Advances in neural information processing systems, 32, 2019.
[46] Raanan Yehezkel and Boaz Lerner. Bayesian network structure learning by recursive autonomy identification. Journal of Machine Learning Research, 10(7), 2009.
[47] Jiji Zhang. On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias. Artificial Intelligence, 172(16-17):1873-1896, 2008.
[48] Kun Zhang, Jonas Peters, Dominik Janzing, and Bernhard Scholkopf. Kernel-based conditional independence test and application in causal discovery. In Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence, pages 804-813, 2011.