Cost-Optimal Learning of Causal Graphs
Authors: Murat Kocaoglu, Alex Dimakis, Sriram Vishwanath
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we test our greedy algorithm to construct intervention designs over randomly sampled chordal graphs. We follow the sampling scheme proposed by Shanmugam et al. (2015) (See the supplementary material for details). The costs of the vertices of the graph are selected from i.i.d. samples of an exponential random variable with mean 1. The total cost of all variables is then the same as the number of variables n in expectation. We normalize the cost incurred by our algorithm with n and compare this normalized cost for different regimes. |
| Researcher Affiliation | Academia | 1The University of Texas at Austin, Austin, Texas, USA. |
| Pseudocode | Yes | Algorithm 1 Greedy Intervention Design for Total Cost Minimization for Chordal Skeleton; Algorithm 2 Greedy Intervention Design for Total Cost Minimization for Interval Skeleton |
| Open Source Code | No | The paper does not provide any links to open-source code or explicitly state that the code for the described methodology is available. |
| Open Datasets | Yes | We follow the sampling scheme proposed by Shanmugam et al. (2015) (See the supplementary material for details). |
| Dataset Splits | No | The paper does not provide specific details about training, validation, or test dataset splits. It mentions |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, memory, or cloud instance types used for running the experiments. |
| Software Dependencies | No | The paper mentions implementing "the linear-time algorithm by Frank (Frank, 1975)" but does not specify any software names with version numbers (e.g., libraries, frameworks, or programming language versions). |
| Experiment Setup | Yes | In this section, we test our greedy algorithm to construct intervention designs over randomly sampled chordal graphs. We follow the sampling scheme proposed by Shanmugam et al. (2015) (See the supplementary material for details). The costs of the vertices of the graph are selected from i.i.d. samples of an exponential random variable with mean 1. The total cost of all variables is then the same as the number of variables n in expectation. We normalize the cost incurred by our algorithm with n and compare this normalized cost for different regimes. The parameter d is a parameter that determines the sparsity of the graph: Graphs with larger d are expected to have more edges. We limit the simulation to at most 10 experiments (x-axis) and observe the effect of changing the number of variables n and parameter d. |