Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Addressing Hidden Confounding with Heterogeneous Observational Datasets for Recommendation
Authors: Yanghao Xiao, Haoxuan Li, Yongqiang Tang, Wensheng Zhang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three public datasets validate our method achieves state-of-the-art performance in the presence of hidden confounding, regardless of RCT data availability. (...) 4 Experiments |
| Researcher Affiliation | Academia | Yanghao Xiao1,3 Haoxuan Li2 Yongqiang Tang3, Wensheng Zhang4, 1University of Chinese Academy of Sciences 2Peking University 3Institute of Automation, Chinese Academy of Sciences 4Guangzhou University |
| Pseudocode | Yes | Algorithm 1: The Proposed Meta Debias Learning Algorithm |
| Open Source Code | No | The paper states 'The paper provides open access to the data and code' in its NeurIPS checklist, but does not provide a specific link or explicit instruction for accessing the code within the main paper content or appendix that the user can directly act on. |
| Open Datasets | Yes | we conduct extensive experiments on three public datasets, COAT2, YAHOO! R33, and KUAIREC4 [11]. 2https://www.cs.cornell.edu/~schnabts/mnar/ 3https://webscope.sandbox.yahoo.com 4https://github.com/chongminggao/Kuai Rec |
| Dataset Splits | Yes | Following previous works [3, 28, 29, 34], we randomly split 5% unbiased data from the test set as validation set, and for all methods requiring RCT data, we employ observational data without hidden confounding to pretend RCT data. |
| Hardware Specification | Yes | All the methods are implemented on Py Torch with Adam as the optimizer and NVIDIA A40 as the computing resource, and we tune learning rate in {0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05} and weight decay in [1e-7, 10]. |
| Software Dependencies | No | All the methods are implemented on Py Torch with Adam as the optimizer and NVIDIA A40 as the computing resource, and we tune learning rate in {0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05} and weight decay in [1e-7, 10]. It mentions 'Py Torch' but does not specify a version number. |
| Experiment Setup | Yes | Two-layer multi-layer perceptron are used as the base model, and we compare proposed methods with both RCT data-free and RCT data-based methods. (...) All the methods are implemented on Py Torch with Adam as the optimizer and NVIDIA A40 as the computing resource, and we tune learning rate in {0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05} and weight decay in [1e-7, 10]. |