Randomized Experimental Design for Causal Graph Discovery
Authors: Huining Hu, Zhentao Li, Adrian R Vetta
NeurIPS 2014 | Conference 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. huining.hu@mail.mcgill.ca Zhentao Li LIENS, Ecole Normale Sup erieure zhentao.li@ens.fr Adrian Vetta Department of Mathematics and Statistics and School of Computer Science, Mc Gill University. vetta@math.mcgill.ca |
| 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. |