[1] A. M. Alaa and M. van der Schaar. Limits of estimating heterogeneous treatment effects: Guidelines for practical algorithm design. In Proceedings of the 35th International Conference on Machine Learning, volume 80, pages 129–138, Stockholm, Sweden, 2018.
[2] S. M. Ali and S. D. Silvey. A general class of coefficients of divergence of one distribution from another. Journal of the Royal Statistical Society: Series B (Methodological), 28(1):131-142, 1966.
[3] S. Assaad, S. Zeng, C. Tao, S. Datta, N. Mehta, R. Henao, F. Li, and L. Carin. Counterfactual representation learning with balancing weights. In The 24th International Conference on Artificial Intelligence and Statistics, volume 130 of Proceedings of Machine Learning Research, pages 1972–1980, Virtual Event, 2021.
[4] S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. W. Vaughan. A theory of learning from different domains. Mach. Learn., 79(1-2):151-175, 2010.
[5] S. Ben-David, J. Blitzer, K. Crammer, and F. Pereira. Analysis of representations for domain adaptation. In Advances in Neural Information Processing Systems 19, pages 137-144, British Columbia, Canada, 2006. MIT Press.
[6] A. Betlei, E. Diemert, and M. Amini. Treatment targeting by AUUC maximization with generalization guarantees. CoRR, abs/2012.09897, 2020.
[7] I. Bica, J. Jordon, and M. van der Schaar. Estimating the effects of continuous-valued interventions using generative adversarial networks. In Advances in Neural Information Processing Systems 33, virtual event, 2020.
[8] L. Buesing, T. Weber, Y. Zwols, N. Heess, S. Racanière, A. Guez, and J. Lespiau. Woulda, coulda, shoulda: Counterfactually-guided policy search. In 7th International Conference on Learning Representations, New Orleans, LA, 2019.
[9] J. Byrd and Z. C. Lipton. What is the effect of importance weighting in deep learning? In Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, pages 872-881, Long Beach, CA, 2019.
[10] X. Chen, B. He, Y. Yu, Q. Li, Z. T. Qin, W. Shang, J. Ye, and C. Ma. Sim2rec: A simulator-based decision-making approach to optimize real-world long-term user engagement in sequential recommender systems. In 39th IEEE International Conference on Data Engineering, pages 3389-3402, Anaheim, CA, 2023. IEEE.
[11] X. Chen, S. Li, H. Li, S. Jiang, Y. Qi, and L. Song. Generative adversarial user model for reinforcement learning based recommendation system. In Proceedings of the 36th International Conference on Machine Learning, pages 1052-1061, Long Beach, CA, 2019.
[12] X. Chen, Y. Yu, Q. Li, F. Luo, Z. T. Qin, W. Shang, and J. Ye. Offline model-based adaptable policy learning. In M. Ranzato, A. Beygelzimer, Y. N. Dauphin, P. Liang, and J. W. Vaughan, editors, Advances in Neural Information Processing Systems 34, pages 8432-8443, virtual, 2021.
[13] X.-H. Chen, F.-M. Luo, Y. Yu, Q. Li, Z. Qin, W. Shang, and J. Ye. Offline model-based adaptable policy learning for decision-making in out-of-support regions. IEEE Transactions on Pattern Analysis Machine Intelligence, (01):1-16, 5555.
[14] X.-H. Chen, Y. Yu, Q. Li, F.-M. Luo, Z. T. Qin, S. Wenjie, and J. Ye. Offline model-based adaptable policy learning. In Advances in Neural Information Processing Systems 34, Virtual Conference, 2021.