Caizhi Tang

According to our database, Caizhi Tang authored at least 14 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of three.

2024

Invariant Graph Learning for Causal Effect Estimation.

Yongduo Sui, Caizhi Tang, Zhixuan Chu, Junfeng Fang, Yuan Gao, Qing Cui, Longfei Li, Jun Zhou, Xiang Wang

Proceedings of the ACM on Web Conference 2024, 2024

Domain Level Interpretability: Interpreting Black-box Model with Domain-specific Embedding.

Ya-Lin Zhang, Caizhi Tang, Lu Yu, Jun Zhou, Longfei Li, Qing Cui, Fangfang Fan, Linbo Jiang, Xiaosong Zhao

Proceedings of the 17th ACM International Conference on Web Search and Data Mining, 2024

Combining Incomplete Observational and Randomized Data for Heterogeneous Treatment Effects.

Dong Yao, Caizhi Tang, Qing Cui, Longfei Li

Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024

Backdoor Adjustment via Group Adaptation for Debiased Coupon Recommendations.

Junpeng Fang, Gongduo Zhang, Qing Cui, Caizhi Tang, Lihong Gu, Longfei Li, Jinjie Gu, Jun Zhou

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

GINT: A Generative Interpretability method via perturbation in the latent space.

Caizhi Tang, Qing Cui, Longfei Li, Jun Zhou

Expert Syst. Appl., December, 2023

DGBCT: A Scalable Distributed Gradient Boosting Causal Tree at Alipay.

Jun Zhou, Caizhi Tang, Qing Cui, Yi Ding, Longfei Li, Fei Wu

Proceedings of the Companion Proceedings of the ACM Web Conference 2023, 2023

Alleviating Matching Bias in Marketing Recommendations.

Junpeng Fang, Qing Cui, Gongduo Zhang, Caizhi Tang, Lihong Gu, Longfei Li, Jinjie Gu, Jun Zhou, Fei Wu

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023

FAST: a Fused and Accurate Shrinkage Tree for Heterogeneous Treatment Effects Estimation.

Jia Gu, Caizhi Tang, Han Yan, Qing Cui, Longfei Li, Jun Zhou

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Difference-in-Differences Meets Tree-based Methods: Heterogeneous Treatment Effects Estimation with Unmeasured Confounding.

Caizhi Tang, Huiyuan Wang, Xinyu Li, Qing Cui, Longfei Li, Jun Zhou

Proceedings of the International Conference on Machine Learning, 2023

2022

Interpreting Model Predictions with Constrained Perturbation and Counterfactual Instances.

Jun-Peng Fang, Jun Zhou, Qing Cui, Caizhi Tang, Longfei Li

Int. J. Pattern Recognit. Artif. Intell., 2022

Debiased Causal Tree: Heterogeneous Treatment Effects Estimation with Unmeasured Confounding.

Caizhi Tang, Huiyuan Wang, Xinyu Li, Qing Cui, Ya-Lin Zhang, Feng Zhu, Longfei Li, Jun Zhou, Linbo Jiang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Semi-Supervised Learning with Data Augmentation for Tabular Data.

Jun-Peng Fang, Caizhi Tang, Qing Cui, Feng Zhu, Longfei Li, Jun Zhou, Wei Zhu

Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

2020

AutoRec: A Comprehensive Platform for Building Effective and Explainable Recommender Models.

Qing Cui, Qitao Shi, Hao Qian, Caizhi Tang, Xixi Li, Yiming Zhao, Tao Jiang, Longfei Li, Jun Zhou

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track, 2020

2019

AntProphet: an Intention Mining System behind Alipay's Intelligent Customer Service Bot.

Cen Chen, Xiaolu Zhang, Sheng Ju, Chilin Fu, Caizhi Tang, Jun Zhou, Xiaolong Li

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019