Model-Powered Conditional Independence Test
Authors: Rajat Sen, Ananda Theertha Suresh, Karthikeyan Shanmugam, Alexandros G. Dimakis, Sanjay Shakkottai
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically validate the performance of our algorithm on simulated and real datasets and show performance gains over previous methods. |
| Researcher Affiliation | Collaboration | Rajat Sen1,*, Ananda Theertha Suresh2,*, Karthikeyan Shanmugam3,*, Alexandros G. Dimakis1, and Sanjay Shakkottai1 1The University of Texas at Austin 2Google, New York 3IBM Research, Thomas J. Watson Center |
| Pseudocode | Yes | Algorithm 1 Data Gen ... Algorithm 2 CCITv1 ... Algorithm 3 CCITv2 |
| Open Source Code | Yes | The python package for our implementation can be found here (https://github.com/rajatsen91/CCIT). |
| Open Datasets | Yes | We also apply our algorithm for analyzing CI relations in the protein signaling network data from the flow cytometry data-set [26]. ... Karen Sachs, Omar Perez, Dana Pe er, Douglas A Lauffenburger, and Garry P Nolan. Causal protein-signaling networks derived from multiparameter single-cell data. Science, 308(5721):523 529, 2005. |
| Dataset Splits | Yes | Divide data-set D into train and test set Dr and De respectively. Note that |Dr| = |De| = n. |
| Hardware Specification | No | The paper does not specify any hardware components such as CPU, GPU, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'XGBoost [6]' and a 'python package for our implementation' but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | In these experiments the number of bootstraps per data-set for CCIT was set to B = 50. We set the threshold in Algorithm 3 to = 1/pn, which is an upper-bound on the expected variance of the test-statistic when H0 holds. |