Situation Testing-Based Discrimination Discovery: A Causal Inference Approach
Authors: Lu Zhang, Yongkai Wu, Xintao Wu
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through empirical assessments on a real dataset, our approach shows good efficacy both in accuracy and efficiency. To evaluate the proposed discrimination discovery algorithm, we have conducted experiments by using the Dutch Census of 2001 |
| Researcher Affiliation | Academia | Lu Zhang, Yongkai Wu, and Xintao Wu University of Arkansas {lz006,yw009,xintaowu}@uark.edu |
| Pseudocode | Yes | Algorithm 1: Discrimination Discovery (CBN-DD) |
| Open Source Code | No | The paper does not provide any explicit statement about releasing open-source code for its methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | To evaluate the proposed discrimination discovery algorithm, we have conducted experiments by using the Dutch Census of 2001 [Netherlands, 2001]. The reference [Netherlands, 2001] points to: Statistics Netherlands. Volkstelling. https://sites.google.com/site/faisalkamiran/, 2001. |
| Dataset Splits | No | The paper describes how test data was constructed ('We randomly select 200 tuples as the targets for discrimination testing.', 'We manually modify the dataset to obtain a data with ground truth...'), but it does not specify any training or validation dataset splits (e.g., 80/10/10 split or k-fold cross-validation for training). |
| Hardware Specification | No | The paper mentions 'The average CPU time for one target tuple is 0.3s for CBNDD and 20.3s for KNN-DD' but does not specify any particular CPU model, memory, or other hardware details. |
| Software Dependencies | No | The CBN is constructed by TETRAD [Glymour and others, 2004], an open-source platform for causal modeling. We employ the original PC algorithm [Spirtes et al., 2000]. The paper mentions software tools used (TETRAD, PC algorithm) but does not provide specific version numbers for them. |
| Experiment Setup | Yes | The threshold is set as 0.05. We employ the original PC algorithm [Spirtes et al., 2000] and the significance level = 0.05 for the structure learning. The paper also discusses varying the parameter K (e.g., K=10, 50, 90). |