Randomized Experimental Design for Causal Graph Discovery
Authors: Huining Hu, Zhentao Li, Adrian R Vetta
NeurIPS 2014 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we present computer simulations to complement our theoretic results. |
| Researcher Affiliation | Academia | Huining Hu School of Computer Science, Mc Gill University. EMAIL Zhentao Li LIENS, Ecole Normale Sup erieure EMAIL Adrian Vetta Department of Mathematics and Statistics and School of Computer Science, Mc Gill University. EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of its source code. |
| Open Datasets | No | The paper describes generating its own random causal graphs using the Erd os-R enyi model ("For the simulations, we first generate a random causal graph G in the E-R model."), rather than using a pre-existing publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper describes simulation parameters for generating graphs but does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | The paper mentions that simulations were conducted in MATLAB but does not specify any hardware details such as CPU, GPU, or memory used. |
| Software Dependencies | No | The paper mentions that simulations were conducted in MATLAB, but does not specify a version number for MATLAB or any other software dependencies. |
| Experiment Setup | Yes | For the simulations, we first generate a random causal graph G in the E-R model. We ran simulations for four choices of probability p, specifically p {0.8, 0.5, 0.1, 0.01}, and for four choices of graph size n, specifically n {500, 1000, 5000, 15000}. For each combination pair {n, p} we ran 1000 simulations. |