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].
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. |