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..
On the Fairness of Causal Algorithmic Recourse
Authors: Julius von Kügelgen, Amir-Hossein Karimi, Umang Bhatt, Isabel Valera, Adrian Weller, Bernhard Schölkopf9584-9594
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We study theoretically and empirically how to enforce fair causal recourse by altering the classifier and perform a case study on the Adult dataset. |
| Researcher Affiliation | Academia | 1 Max Planck Institute for Intelligent Systems, T ubingen, Germany 2 University of Cambridge 3 ETH Z urich 4 Saarland University 5 The Alan Turing Institute |
| Pseudocode | No | The paper describes optimization problems (e.g., Eq. 1, 2, 3) and solution approaches (e.g., 'using brute force search'), but it does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | Code to reproduce our experiments is available at https://github.com/amirhk/recourse. |
| Open Datasets | Yes | We use the Adult dataset (Lichman et al. 2013), which consists of 45k+ samples without missing data. |
| Dataset Splits | Yes | select all remaining hyperparameters (including the trade-off parameter λ for Fair SVM) using 5-fold cross validation. |
| Hardware Specification | No | The paper describes numerical simulations and a case study on the Adult dataset but does not provide specific details regarding the hardware used for these experiments. |
| Software Dependencies | No | The paper mentions training "linear and nonlinear logistic regression (LR), and different support vector machines (SVMs)" and using a "probabilistic framework of Karimi et al. (2020c)", but it does not specify exact version numbers for any software libraries or dependencies. |
| Experiment Setup | Yes | We use either a linear or polynomial kernel for all SVMs (depending on the GT labels) and select all remaining hyperparameters (including the trade-off parameter λ for Fair SVM) using 5-fold cross validation. |