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..
Causal Strategic Learning with Competitive Selection
Authors: Kiet Q. H. Vo, Muneeb Aadil, Siu Lun Chau, Krikamol Muandet
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Lastly, we complement our theoretical results with simulation studies. Our results highlight not only the importance of causal modeling as a strategy to mitigate the effect of gaming, as suggested by previous work, but also the need of a benevolent regulator to enable it. |
| Researcher Affiliation | Academia | Kiet Q. H. Vo1,2, Muneeb Aadil1,2, Siu Lun Chau1, Krikamol Muandet1 1CISPA Helmholtz Center for Information Security, Saarbr ucken, Germany 2Saarland University, Saarbr ucken, Germany |
| Pseudocode | Yes | Algorithm 1: Mean-shift Linear Regression (MSLR) |
| Open Source Code | Yes | The code to reproduce our experiments is publicly available.2 https://github.com/muandet-lab/csl-with-selection |
| Open Datasets | No | Following Harris et al. (2022), we generate a synthetic college admission dataset. |
| Dataset Splits | No | No specific dataset split information (percentages, counts, or methodology for train/validation/test) is provided in the paper. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments are provided in the paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | Precisely, the student is admitted into college i if their prediction หyit lies within the top ฯ-percentile of all applicants where ฯ [0, 1] and we set ฯ = 0.5. Further discussion of this variant of ranking selection is included in Appendix F.1. As ranking selection (Definition 1) requires access to the distribution p(X t ฮธit), we estimate it by simulating 1000 students in each round. |