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..
Efficient Intervention Design for Causal Discovery with Latents
Authors: Raghavendra Addanki, Shiva Kasiviswanathan, Andrew Mcgregor, Cameron Musco
ICML 2020 | Venue PDF | 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. |