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..
Model-Powered Conditional Independence Test
Authors: Rajat Sen, Ananda Theertha Suresh, Karthikeyan Shanmugam, Alexandros G. Dimakis, Sanjay Shakkottai
NeurIPS 2017 | Venue PDF | 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. |