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].
Covariate Shift Corrected Conditional Randomization Test
Authors: Bowen Xu, Yiwen Huang, Chuan Hong, Shuangning Li, Molei Liu
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, through simulation studies, we demonstrate that our method not only maintains control over Type-I errors but also exhibits superior power, confirming its efficacy and practical utility in real-world scenarios where covariate shifts are prevalent. Finally, we apply our methodology to a real-world dataset to assess the impact of a COVID-19 treatment on the 90-day mortality rate among patients. |
| Researcher Affiliation | Academia | Bowen Xu Harvard University EMAIL Yiwen Huang Department of Statistics Peking University EMAIL Chuan Hong Department of Biostatistics and Bioinformatics Duke University EMAIL Shuangning Li Booth School of Business University of Chicago EMAIL Molei Liu: Department of Biostatistics Columbia Mailman School of Public Health EMAIL |
| Pseudocode | Yes | Algorithm 1 Covariate Shift Corrected PCR (cs PCR) Test. Algorithm 2 Covariate Shift Corrected PCR Test with Power Enhancement. |
| Open Source Code | Yes | Replication code for our simulation studies is submitted as supplementary material. It will also be made publicly available on Git Hub once our paper is accepted. |
| Open Datasets | No | The paper mentions using a real-world dataset from Duke University Health System for the COVID-19 application, but it is not publicly available. For simulations, data is generated, not from a specific public dataset. |
| Dataset Splits | Yes | We generate 1000 unlabeled source and target samples to estimate the density ratio and generate 500 labeled source samples for testing. |
| Hardware Specification | Yes | All experiments run on a Macbook Pro 2022 M2. |
| Software Dependencies | Yes | We estimate the covariance matrix of the sequence of Wi s using the Monte Carlo method and use the momentchi2 package [1] for calculating the p-value. |
| Experiment Setup | Yes | Additionally, we empirically choose the best hyperparameter L 3 for all our experiments through additional experiments shown in Appendix B.2. |