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 [1].
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. |