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 [1].
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. |