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 [1].
Fairness on Principal Stratum: A New Perspective on Counterfactual Fairness
Authors: Haoxuan Li, Zeyu Tang, Zhichao Jiang, Zhuangyan Fang, Yue Liu, Zhi Geng, Kun Zhang
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments are conducted using synthetic and real-world datasets to verify the effectiveness of our methods. |
| Researcher Affiliation | Collaboration | 1Peking University 2Carnegie Mellon University 3Sun Yatsen University 4Xiaomi 5Renmin University of China 6Beijing Technology and Business University 7Mohamed bin Zayed University of Artificial Intelligence. |
| Pseudocode | No | The paper describes methods in text but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks or figures. |
| Open Source Code | No | The paper does not provide any statements about releasing code, nor does it include links to source code repositories. |
| Open Datasets | Yes | The STUDENTINFO file in the Open University Learning Analytics Dataset (OULAD) dataset (Kuzilek et al., 2017) is used for the real-world experiment. |
| Dataset Splits | No | The paper mentions a sample size of 1,000 for synthetic data and 32,593 students for the OULAD dataset. It discusses dividing the population into subgroups for analysis, but does not provide specific train/test/validation split percentages, counts, or methodologies needed to reproduce the data partitioning for model training. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types) used for running the experiments. |
| Software Dependencies | No | The paper mentions using "the PC algorithm in the causal-learn package" and various models like "Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF) and Naive Bayes (NB)", but it does not specify version numbers for any of these software components or libraries. |
| Experiment Setup | No | The paper mentions data generation parameters such as "noise ϵi N(0, 2.5)" and "n is the sample size, which is 1,000", but it does not provide specific experimental setup details like hyperparameter values (e.g., learning rates, batch sizes, number of epochs) or other training configurations for the models used. |