https://github.com/pat-alt/endogenous-macrodynamics-in-algorithmic-recourse

Repository for "Endogenous Macrodynamics in Algorithmic Recourse" (Altmeyer et al., 2023)
https://github.com/pat-alt/endogenous-macrodynamics-in-algorithmic-recourse

algorithmic-recourse counterfactual-explanations explainable-ai julia

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Repository for "Endogenous Macrodynamics in Algorithmic Recourse" (Altmeyer et al., 2023)

• Host: GitHub
• URL: https://github.com/pat-alt/endogenous-macrodynamics-in-algorithmic-recourse
• Owner: pat-alt
• Created: 2022-12-19T13:37:51.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2025-05-27T13:34:40.000Z (4 months ago)
• Last Synced: 2025-06-08T00:37:34.496Z (4 months ago)
• Topics: algorithmic-recourse, counterfactual-explanations, explainable-ai, julia
• Language: HTML
• Homepage: http://www.patalt.org/endogenous-macrodynamics-in-algorithmic-recourse/
• Size: 143 MB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          

# Endogenous Macrodynamics in Algorithmic Recourse

This repository contains all code, notebooks, data and empirical results

for our conference paper “Endogenous Macrodynamics in Algorithmic

Recourse” (Altmeyer et al. 2023).

Below is a list of relevant resources hosted in this repository:

1.  [Paper](paper/paper.pdf) in this repo

2.  [Paper](https://openreview.net/pdf?id=-LFT2YicI9v) on OpenReview

3.  [Online

    Companion](https://www.paltmeyer.com/endogenous-macrodynamics-in-algorithmic-recourse/)

4.  [IEEE SaTML Presentation

    Slides](https://www.paltmeyer.com/content/talks/posts/2023-ieee-satml/presentation.html)

5.  [IEEE SaTML Poster](dev/poster/poster.pdf)

6.  Software:

    [`AlgorithmicRecourseDynamics.jl`](https://github.com/pat-alt/AlgorithmicRecourseDynamics.jl)

    and

    [`CounterfactualExplanations.jl`](https://github.com/pat-alt/CounterfactualExplanations.jl)

## Motivation

The chart below illustrates what we define as macrodynamics in

Algorithmic Recourse: (a) we have a simple linear classifier trained for

binary classification where samples from the negative class (*y* = 0)

are marked in orange and samples of the positive class (*y* = 1) are

marked in blue; (b) the implementation of AR for a random subset of

individuals leads to a noticeable domain shift; (c) as the classifier is

retrained we observe a corresponding model shift; (d) as this process is

repeated, the decision boundary moves away from the target class.

![](paper/www/poc.png)

## Abstract

Existing work on Counterfactual Explanations (CE) and Algorithmic

Recourse (AR) has largely focused on single individuals in a static

environment: given some estimated model, the goal is to find valid

counterfactuals for an individual instance that fulfill various

desiderata. The ability of such counterfactuals to handle dynamics like

data and model drift remains a largely unexplored research challenge.

There has also been surprisingly little work on the related question of

how the actual implementation of recourse by one individual may affect

other individuals. Through this work, we aim to close that gap. We first

show that many of the existing methodologies can be collectively

described by a generalized framework. We then argue that the existing

framework does not account for a hidden external cost of recourse, that

only reveals itself when studying the endogenous dynamics of recourse at

the group level. Through simulation experiments involving various

state-of-the-art counterfactual generators and several benchmark

datasets, we generate large numbers of counterfactuals and study the

resulting domain and model shifts. We find that the induced shifts are

substantial enough to likely impede the applicability of Algorithmic

Recourse in some situations. Fortunately, we find various strategies to

mitigate these concerns. Our simulation framework for studying recourse

dynamics is fast and open-sourced.

## Key Findings

- Our findings indicate that state-of-the-art approaches to Algorithmic

  Recourse induce substantial domain and model shifts.

- We would argue that the expected external costs of individual recourse

  should be shared by all stakeholders.

- A straightforward way to achieve this is to penalize external costs in

  the counterfactual search objective function (Equation 4).

- Various simple strategies based on this notion can be effectively used

  to mitigate shifts.

## Proposed Mitigation Strategies

By introducing a second penalty term in the counterfactual search

objective, we can explicitly penalize external costs. The figure below

illustrates how the mitigation strategies compared to the baseline

approach, that is, Wachter (Generic) with γ = 0.5: choosing a higher

decision threshold pushes the counterfactual a little further into the

target domain; this effect is even stronger for ClaPROAR; finally, using

the Gravitational generator the counterfactual ends up all the way

inside the target domain. Find out more in the [paper](paper/paper.pdf).

![](paper/www/mitigation.png)

## References





Altmeyer, Patrick, Giovan Angela, Aleksander Buszydlik, Karol Dobiczek,

Arie van Deursen, and Cynthia Liem. 2023. “Endogenous Macrodynamics in

Algorithmic Recourse.” In *First IEEE Conference on Secure and

Trustworthy Machine Learning*.