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..
Interventional Fairness on Partially Known Causal Graphs: A Constrained Optimization Approach
Authors: Aoqi Zuo, Yiqing Li, Susan Wei, Mingming Gong
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Results on both simulated and real-world datasets demonstrate the effectiveness of this method. |
| Researcher Affiliation | Academia | Aoqi Zuo1, Yiqing Li1,2,3, Susan Wei1 & Mingming Gong1,3 1School of Mathematics and Statistics, The University of Melbourne 2School of Data Science, Fudan University 3Mohamed bin Zayed University of Artificial Intelligence EMAIL EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1 Construct MPDAG (Perkovic et al., 2017; Meek, 1995) and Algorithm 2 Partial causal ordering (PCO) (Perkovic, 2020, Algorithm 1) |
| Open Source Code | No | The paper does not explicitly state that open-source code for the methodology is provided, nor does it include a link to a code repository. |
| Open Datasets | Yes | Our first experiment with real-world data is based on the UCI Student Performance Data Set (Cortez & Silva, 2008) and The task of credit risk assessment involves predicting the likelihood of a borrower defaulting on a loan. For our experiment, we utilize the Credit Risk Dataset... The causal graphs for the Credit Risk Dataset is provided in Figure 13. The attribute information can be found at https://www.kaggle.com/datasets/laotse/credit-risk-dataset. |
| Dataset Splits | Yes | The proportion of training, validation and test data is split as 8 1 1. |
| Hardware Specification | No | The paper mentions using 'the LIEF HPC-GPGPU Facility hosted at the University of Melbourne' but does not specify exact hardware components like GPU models, CPU models, or memory details. |
| Software Dependencies | No | The paper mentions software like TETRAD and GES algorithm, and mixture density networks, but does not specify version numbers for these or other software dependencies. |
| Experiment Setup | Yes | The λ in our optimisation problem is [0, 0.5, 5, 20, 60, 100]. and Additionally, in our ϵ-IFair model, the λ is set to be [0, 1, 40, 100, 130, 175, 250]. and For each model, we run it 20 times with different seeds and report the average results in the main section. |