Covariate Shift Corrected Conditional Randomization Test
Authors: Bowen Xu, Yiwen Huang, Chuan Hong, Shuangning Li, Molei Liu
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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 bowenxu@g.harvard.edu Yiwen Huang Department of Statistics Peking University 2000010773@stu.pku.edu.cn Chuan Hong Department of Biostatistics and Bioinformatics Duke University chuan.hong@duke.edu Shuangning Li Booth School of Business University of Chicago shuangning.li@chicagobooth.edu Molei Liu: Department of Biostatistics Columbia Mailman School of Public Health ml4890@cumc.columbia.edu |
| 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. |