A New Paradigm for Counterfactual Reasoning in Fairness and Recourse
Authors: Lucius E.J. Bynum, Joshua R. Loftus, Julia Stoyanovich
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments In this section, we demonstrate our criteria computationally on real and simulated data. |
| Researcher Affiliation | Academia | Lucius E.J. Bynum1 , Joshua R. Loftus2 and Julia Stoyanovich1,3 1New York University, Center for Data Science 2London School of Economics, Department of Statistics 3New York University, Tandon School of Engineering lucius@nyu.edu, J.R.Loftus@lse.ac.uk, stoyanovich@nyu.edu |
| Pseudocode | Yes | Algorithm 1 Backtracking Counterfactual Sampling |
| Open Source Code | Yes | Code to reproduce all our experiments is available online.5 Our code repository is located on Git Hub at: https://github.com/ lbynum/backtracking-counterfactual-fairness-and-recourse. |
| Open Datasets | Yes | Used to illustrate counterfactual fairness in [Kusner et al., 2017], the law school dataset from [Wightman, 1998] contains information on law students demographics and academic performance compiled from a 1998 Law School Admission Council survey. |
| Dataset Splits | No | The paper mentions 'datasets of size 500' and 'random sample of 5000 observations' but does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., programming language versions, library versions, solver versions) used in the experiments. |
| Experiment Setup | Yes | We fit several predictors on datasets of size 500 from the above equations, each a linear regression using some subset of the available covariates (excluding A)." and "We generate data for Scenario 2 with a small modification to Scenario 1: A Bern(0.5), ZA N(A/2, 1), ZA N(0, 1), X1 N(2ZA + ZA , 1), X2 N(3ZA , 1), and Y N(X1 + X2 + 2ZA + ZA 2, 1). |