Allocation Requires Prediction Only if Inequality Is Low
Authors: Ali Shirali, Rediet Abebe, Moritz Hardt
ICML 2024 | Conference PDF | Archive PDF | Plain Text | 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. |