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Software and data underlying the publication: Endogenous Macrodynamics in Algorithmic Recourse

Dataset

Description

Code and research results for SaTML 2023 research paper. Originally released here: https://github.com/pat-alt/endogenous-macrodynamics-in-algorithmic-recourse.


The research results include:


Folders with images that went into a) the body of the paper or b) the online companion.
Folders with results (.jls; .csv) for different experiments: a) synthetic data; b) real-world data; and, c) mitigation strategies for both categories of datasets (see paper for details on experiments). Results for all categories are further grouped by dataset.
For each dataset, results include: a) "experiment.jls" files that can be loaded into a Julia session: the loaded Julia objects are structs that contain all settings characterizing a specific experiment. b) "output.csv" files that contain the final experimental outputs: estimated counterfactual evaluation metrics groups by model and counterfactual explainer.
Date made available2025
PublisherTU Delft - 4TU.ResearchData
  • Endogenous Macrodynamics in Algorithmic Recourse

    Altmeyer, P., Giovan, A., Buszydlik, A., Dobiczek, K., van Deursen, A. & Liem, C. C. S., 2023, Proceedings of the 2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML). Ceballos, C. (ed.). Piscataway: IEEE, p. 418-431 14 p.

    Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

    Open Access
    File
    4   Link opens in a new tab Citations (SciVal)
    65 Downloads (Pure)

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