Counterfactual Fairness by Combining Factual and Counterfactual Predictions
Authors: Zeyu Zhou, Tianci Liu, Ruqi Bai, Jing Gao, Murat Kocaoglu, David I. Inouye
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both synthetic and semi-synthetic datasets demonstrate the validity of our analysis and methods. |
| Researcher Affiliation | Academia | Zeyu Zhou, Tianci Liu, Ruqi Bai, Jing Gao, Murat Kocaoglu, David I. Inouye Elmore Family School of Electrical and Computer Engineering Purdue University {zhou1059, liu3351, bai116, jinggao, mkocaoglu, dinouye}@purdue.edu |
| Pseudocode | Yes | Algorithm 1 Plug-in Counterfactual Fairness (PCF) |
| Open Source Code | Yes | Code can be found in https://github.com/inouye-lab/pcf |
| Open Datasets | Yes | In this section, we consider Law School Success dataset [Wightman, 1998] where the sensitive attribute is gender and the target is first-year grade. ... To compute TE, we need access to ground truth counterfactuals. Hence we train a generative model on real dataset to generate semi-synthetic dataset following the method in Zuo et al. [2023]. |
| Dataset Splits | No | The paper mentions training, testing, and sometimes evaluating on a subset of data (like the Law School dataset), but it does not explicitly provide specific splits like "80/10/10 split" or sample counts for train/validation/test sets. It states "Given a test set Dtest" for metrics but not how this test set is formed in relation to other splits. |
| Hardware Specification | Yes | All GPU related experiments are run on RTX A5000. |
| Software Dependencies | No | In our synthetic experiments, we mainly use KNN based predictors. We use the default parameters in scikit-learn. All MLP methods uses a structure with hidden layer (20, 20) and Tanh activation. In semi-synthetic experiments, we use MLP methods uses a structure with hidden layer (5, 5) and Tanh activation as this is closer to the ground truth SCM. The paper mentions tools like scikit-learn but does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | All MLP methods uses a structure with hidden layer (20, 20) and Tanh activation. In semi-synthetic experiments, we use MLP methods uses a structure with hidden layer (5, 5) and Tanh activation as this is closer to the ground truth SCM. |