Causal Inference with Conditional Instruments using Deep Generative Models D Cheng, Z Xu, J Li, L Liu, J Liu, TD Le AAAI Conference on Artificial Intelligence (AAAI 2023), 7122-7130, 2023 | 14 | 2023 |
Disentangled Representation for Causal Mediation Analysis Z Xu, D Cheng, J Li, J Liu, L Liu, K Wang AAAI Conference on Artificial Intelligence (AAAI 2023), 10666-10674, 2023 | 11 | 2023 |
Causal Inference with Conditional Front-Door Adjustment and Identifiable Variational Autoencoder Z Xu, D Cheng, J Li, J Liu, L Liu, K Yu International Conference on Learning Representations (ICLR 2024), 1-22, 2024 | 10 | 2024 |
Disentangled Representation with Causal Constraints for Counterfactual Fairness Z Xu, J Liu, D Cheng, J Li, L Liu, K Wang Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2023 …, 2023 | 8 | 2023 |
Instrumental Variable Estimation for Causal Inference in Longitudinal Data with Time-Dependent Latent Confounders D Cheng, Z Xu, J Li, L Liu, J Liu, W Gao, TD Le AAAI Conference on Artificial Intelligence (AAAI 2024), 11480-11488, 2024 | 7 | 2024 |
Learning Conditional Instrumental Variable Representation for Causal Effect Estimation D Cheng, Z Xu, J Li, L Liu, TD Le, J Liu Joint European Conference on Machine Learning and Knowledge Discovery in …, 2023 | 5 | 2023 |
Conditional Instrumental Variable Regression with Representation Learning for Causal Inference D Cheng, Z Xu, J Li, L Liu, J Liu, TD Le International Conference on Learning Representations (ICLR 2024), 1-17, 2024 | 4 | 2024 |
Assessing Classifier Fairness with Collider Bias Z Xu, Z Xu, J Liu, D Cheng, J Li, L Liu, K Wang Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2022 …, 2022 | 4 | 2022 |
Disentangled Latent Representation Learning for Tackling the Confounding M-Bias Problem in Causal Inference D Cheng, Y Xie, Z Xu, J Li, L Liu, J Liu, Y Zhang, Z Feng IEEE International Conference on Data Mining (ICDM 2023), 51-60, 2023 | 2 | 2023 |
Off-policy Evaluation for Multiple Actions in the Presence of Unobserved Confounders H Wang, L Liu, J Li, Z Xu, J Liu, Z Cao, D Cheng The ACM Web Conference (WWW 2025), 2025 | | 2025 |
Towards Better Evaluation of Recommendation Algorithms with Bi-directional Item Response Theory Z Xu, C Ma, Y Ren, J Chan, W Shao, F Xia The ACM Web Conference (WWW 2025), 2025 | | 2025 |
Fairness Evaluation with Item Response Theory Z Xu, S Kandanaarachchi, CS Ong, E Ntoutsi The ACM Web Conference (WWW 2025), 2025 | | 2025 |
Disentangled Representation Learning for Causal Inference With Instruments D Cheng, J Li, L Liu, Z Xu, W Zhang, J Liu, TD Le IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 1-14, 2024 | | 2024 |
Leaning Time-Varying Instruments for Identifying Causal Effects in Time-Series Data D Cheng, Z Xu, J Li, L Liu, X Guo, S Zhang arXiv preprint arXiv:2411.17774, 2024 | | 2024 |
TSI: A Multi-view Representation Learning Approach for Time Series Forecasting W Gao, Z Xu, J Li, L Liu, J Liu, TD Le, D Cheng, Y Zhao, Y Chen Australasian Joint Conference on Artificial Intelligence (AJCAI 2024), 291-302, 2024 | | 2024 |
Linking Model Intervention to Causal Interpretation in Model Explanation D Cheng, Z Xu, J Li, L Liu, K Yu, TD Le, J Liu arXiv preprint arXiv:2410.15648, 2024 | | 2024 |
An Item Response Theory-based R Module for Algorithm Portfolio Analysis B Oldfield, S Kandanaarachchi, Z Xu, MA Muņoz arXiv preprint arXiv:2408.14025, 2024 | | 2024 |
Causal Effect Estimation using identifiable Variational AutoEncoder with Latent Confounders and Post-Treatment Variables Y Xie, Z Xu, D Cheng, J Li, L Liu, Y Zhang, Z Feng arXiv preprint arXiv:2408.07219, 2024 | | 2024 |
A Data-Driven Approach to Finding K for K Nearest Neighbor Matching in Average Causal Effect Estimation T Xu, Y Zhang, J Li, L Liu, Z Xu, D Cheng, Z Feng International Conference on Web Information Systems Engineering (WISE 2023 …, 2023 | | 2023 |
Linking a predictive model to causal effect estimation J Li, L Liu, Z Xu, HX Tran, TD Le, J Liu arXiv preprint arXiv:2304.04566, 2023 | | 2023 |