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..
Allocation Requires Prediction Only if Inequality Is Low
Authors: Ali Shirali, Rediet Abebe, Moritz Hardt
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present a simple mathematical model to compare prediction-based versus unit-based allocations. We show that prediction leads to superior allocations only when between-unit inequality is low, and the allocation budget is high. (See Fig. 1 for a high-level view of inequality and budget regimes covered in our results.) Our analyses cover a broad range of settings for the price of prediction, treatment effect heterogeneity, and unit-level statistics learnability. Figure 3. ULA outperforms ILA in a simulated setting with high inequality. Figure 4. ULA outperforms ILA in a real-world high inequality setting. Eight school districts from the greater Los Angeles (LA) area are considered. |
| Researcher Affiliation | Academia | 1University of California, Berkeley 2Harvard Society of Fellows 3Max Planck Institute for Intelligent Systems, T ubingen and T ubingen AI Center. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include a statement or link indicating the release of open-source code for the methodology described. |
| Open Datasets | Yes | Here, we utilize the American Community Survey Children s Education Tabulation,2 an annually updated custom data collection of demographic, economic, social, and housing characteristics about school-age children and their families, developed from the U.S. Census Bureau s 2017-2021 data. 2https://nces.ed.gov/programs/edge/Demographic/ACS |
| Dataset Splits | No | The paper does not provide specific details regarding training, validation, and test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments or simulations. |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as library names with version numbers, needed to replicate the experiments. |
| Experiment Setup | Yes | We consider a similar parameter setting as in the synthetic data example with δ = 0.3, q = 0.3, q 0, p(ϵ) = 0.2 B, and a budget B sufficient to treat half of the units. |