Efficient Intervention Design for Causal Discovery with Latents
Authors: Raghavendra Addanki, Shiva Kasiviswanathan, Andrew Mcgregor, Cameron Musco
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we compare the total number of interventions required to recover causal graph G parameterized by p-colliders (See Section 4) vs. maximum degree utilized by [Kocaoglu et al., 2017b]. ... In our plots (Figure 2), we compare the maximum undirected degree (d) with the maximum number of p-colliders between any pair of nodes (which defines ). We ran each experiment 10 times and plot the mean value along with one standard deviation error bars. |
| Researcher Affiliation | Collaboration | Raghavendra Addanki 1 Shiva Prasad Kasiviswanathan 2 Andrew Mc Gregor 1 Cameron Musco 1 1College of Information and Computer Sciences, University of Massachusetts, Amherst, USA. 2Amazon. |
| Pseudocode | Yes | Algorithm 1 SSMATRIX (V, m) |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code for the described methodology or provide a link to a code repository. |
| Open Datasets | No | We demonstrate our results by considering sparse random graphs generated from the families of: (i) Erdös Rényi random graphs G(n, c/n) for constant c, (ii) Random bipartite graphs generated using G(n1, n2, c/n) model, with partitions L, R and edges directed from L to R, (iii) Random directed trees with degrees of nodes generated from power law distribution. |
| Dataset Splits | No | The paper describes generating synthetic graphs for experiments but does not provide specific train/validation/test dataset splits or any other detailed data partitioning methodology. |
| Hardware Specification | No | The paper does not provide specific hardware details (such as GPU/CPU models or memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | Setup. We demonstrate our results by considering sparse random graphs generated from the families of: (i) Erdös Rényi random graphs G(n, c/n) for constant c, (ii) Random bipartite graphs generated using G(n1, n2, c/n) model, with partitions L, R and edges directed from L to R, (iii) Random directed trees with degrees of nodes generated from power law distribution. In each of the graphs that we consider, we include latent variables by sampling 5% of pairs and adding a latent between them. ... For random bipartite graphs, ... we use equal partition sizes n1 = n2 = n/2 and plot the results for G(n/2, n/2, c/n) for constant c = 5. |