Statistical Inference Under Constrained Selection Bias
Authors: Santiago Cortes-Gomez, Mateo Dulce Rubio, Carlos Miguel Patiño, Bryan Wilder
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform experiments on synthetic and semisynthetic data to test our methods. The results empirically confirm that our framework provides valid bounds for the target estimand and allows effective use of domain knowledge: incorporation of more informative constraints produces tighter bounds. |
| Researcher Affiliation | Collaboration | 1Department of Machine Learning, Carnegie Mellon University 2Department of Statistics, Carnegie Mellon University 3Factored AI. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | All the code for our experiments, together with a guide on how to replicate each one of them, is available at https://github.com/secg5/inference_contrained_distribution_shift. |
| Open Datasets | Yes | We use the Folktables dataset (Ding et al., 2021) which provides an interface for curated US Census data. |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software components or libraries with their version numbers that are required to reproduce the experiments. |
| Experiment Setup | Yes | In the experiments metadata folder are JSON files with the exact hyperparameters used in all the experiments presented in the paper. |