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..
Conditional independence testing under misspecified inductive biases
Authors: Felipe Maia Polo, Yuekai Sun, Moulinath Banerjee
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conduct experiments with artificial and real data, showcasing the usefulness of our theory and methods. |
| Researcher Affiliation | Academia | Felipe Maia Polo Department of Statistics University of Michigan EMAIL Yuekai Sun Department of Statistics University of Michigan EMAIL Moulinath Banerjee Department of Statistics University of Michigan EMAIL |
| Pseudocode | Yes | Algorithm 1: Obtaining p-value for the RBPT |
| Open Source Code | Yes | Code in https://github.com/felipemaiapolo/cit. |
| Open Datasets | Yes | For our subsequent experiments, we employ the car insurance dataset examined by Angwin et al. [2]. |
| Dataset Splits | Yes | the training (resp. test) dataset consists of 800 (resp. 200) entries |
| Hardware Specification | Yes | all in a Mac Book Air 2020 M1. |
| Software Dependencies | No | The paper mentions software like "Python script" and "CatBoost regressor" but does not specify their version numbers or other library versions. |
| Experiment Setup | Yes | We assume α = 10% and ℓ(ˆy, y) = (ˆy y)2. ... every predictor we employ operates on linear regression. ... RESIT employs Spearman s correlation between residuals as a test statistic ... For RBPT2, we use a Cat Boost regressor [26] to yield the Rao-Blackwellized predictor. We resort to logistic regression for estimating the distribution of X | Z used by RBPT, GCM, CRT, and CPT. |