PC-Fairness: A Unified Framework for Measuring Causality-based Fairness
Authors: Yongkai Wu, Lu Zhang, Xintao Wu, Hanghang Tong
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic and real-world datasets show the correctness and effectiveness of our method. |
| Researcher Affiliation | Academia | Yongkai Wu University of Arkansas yw009@uark.edu Lu Zhang University of Arkansas lz006@uark.edu Xintao Wu University of Arkansas xintaowu@uark.edu Hanghang Tong University of Illinois at Urbana-Champaign htong@illinois.edu |
| Pseudocode | No | The paper describes its methods using mathematical formulations and descriptive text but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The datasets and implementation are available at http://tiny.cc/pc-fairness-code. |
| Open Datasets | Yes | For the real-world dataset, we adopt the Adult dataset, which consists of 65,123 records with 11 attributes including edu, sex, income etc. Similar to [14], we select 7 attributes, binarize their values, and build the causal graph. The datasets and implementation are available at http://tiny.cc/pc-fairness-code. |
| Dataset Splits | No | The paper uses synthetic datasets D1 and D2 and the Adult dataset for experiments. It mentions using D1 to 'validate our method' and D2 for 'comparing with previous bounding methods,' but it does not specify explicit train/validation/test splits, percentages, or sample counts for any of these datasets. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, or memory specifications) used to run the experiments. |
| Software Dependencies | No | The paper mentions using 'Tetrad [10]' for building causal models but does not provide specific version numbers for this or any other software dependencies like programming languages, libraries, or frameworks used in the implementation. |
| Experiment Setup | Yes | For the real-world dataset, we adopt the Adult dataset, which consists of 65,123 records with 11 attributes including edu, sex, income etc. Similar to [14], we select 7 attributes, binarize their values, and build the causal graph. Fairness threshold τ is set to 0.1. |