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..
Equality of Opportunity in Classification: A Causal Approach
Authors: Junzhe Zhang, Elias Bareinboim
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate our results through experiments in both real (COMPAS) and synthetic datasets. and 6 Simulations and Experiments |
| Researcher Affiliation | Academia | Junzhe Zhang Purdue University, USA EMAIL Elias Bareinboim Purdue University, USA EMAIL |
| Pseudocode | Yes | Algorithm 1: Find Exp Set; Algorithm 3: Ctf-Fair Learning; Algorithm 2: Causal-SFFS |
| Open Source Code | No | The paper does not include any statement about releasing source code for the methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We demonstrate our results through experiments in both real (COMPAS) and synthetic datasets. and [1] J. Angwin, J. Larson, S. Mattu, and L. Kirchner. Machine bias: There s software used across the country to predict future criminals. and it s biased against blacks. Pro Publica, 23, 2016. |
| Dataset Splits | No | The paper mentions 'validation data' for evaluating predictive accuracy during feature selection ('evaluating the best in-class predictive accuracy for classifiers in { f : ˆ PA ˆY } on the validation data.'), but it does not specify concrete dataset split percentages or counts for training, validation, or test sets. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, or cloud resources) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers (e.g., programming language versions, library versions, or solver versions) used in the experiments. |
| Experiment Setup | No | The paper states that 'Details of the experiments are provided in Appendix C [27]', but this paper itself does not contain specific hyperparameters, training configurations, or system-level settings within its main text. |