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..
Situation Testing-Based Discrimination Discovery: A Causal Inference Approach
Authors: Lu Zhang, Yongkai Wu, Xintao Wu
IJCAI 2016 | Venue PDF | 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 EMAIL |
| 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). |