Counterfactual Fairness: Unidentification, Bound and Algorithm
Authors: Yongkai Wu, Lu Zhang, Xintao Wu
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method in the experiments using both synthetic and real-world datasets, as well as compare with existing methods. The results validate our theory and show the effectiveness of our method. |
| Researcher Affiliation | Academia | Yongkai Wu , Lu Zhang and Xintao Wu University of Arkansas {yw009, lz006, xintaowu}@uark.edu |
| Pseudocode | No | The paper describes methods textually but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete statement or link regarding the availability of source code for the described methodology. |
| Open Datasets | Yes | We also use the Adult dataset [Lichman, 2013] to evaluate these methods in a real-world environment. [...] [Lichman, 2013] M Lichman. UCI Machine Learning Repository. http://archive.ics.uci.edu/ml, 2013. |
| Dataset Splits | Yes | Then, we generate 100,000 examples from this causal model and split the data into training and testing sets with a ratio of 80/20. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper mentions using Logistic Regression (LR) and Support Vector Machine (SVM), and the PC algorithm implemented in Tetrad, but does not provide specific version numbers for these software components or any other dependencies. |
| Experiment Setup | Yes | By default, the discrimination threshold τ is set as 0.05. [...] For the baseline method, we adopt the logistic regression (LR) and support vector machine (SVM). [...] We apply the PC algorithm implemented in Tetrad to build the causal graph while the significant threshold is set as 0.01 for conditional independence testing. |