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..
Active Structure Learning of Causal DAGs via Directed Clique Trees
Authors: Chandler Squires, Sara Magliacane, Kristjan Greenewald, Dmitriy Katz, Murat Kocaoglu, Karthikeyan Shanmugam
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show via synthetic experiments that our algorithm can scale to much larger graphs than most of the related work and achieves better worst-case performance than other scalable approaches. |
| Researcher Affiliation | Collaboration | Chandler Squires LIDS, MIT MIT-IBM Watson AI Lab EMAIL Sara Magliacane MIT-IBM Watson AI Lab IBM Research EMAIL Kristjan Greenewald MIT-IBM Watson AI Lab IBM Research EMAIL Dmitriy Katz MIT-IBM Watson AI Lab IBM Research EMAIL Murat Kocaoglu MIT-IBM Watson AI Lab IBM Research EMAIL Karthikeyan Shanmugam MIT-IBM Watson AI Lab IBM Research EMAIL |
| Pseudocode | Yes | Algorithm 1 DCT POLICY |
| Open Source Code | Yes | A code base to recreate these results can be found at https://github.com/csquires/dct-policy. |
| Open Datasets | No | The paper uses synthetic graphs generated by a described procedure but does not refer to a publicly available dataset with a specific link or citation for access. |
| Dataset Splits | No | No explicit training, validation, or test dataset splits (e.g., percentages, sample counts, or cross-validation setup) were mentioned for the synthetic graph generation and evaluation. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for experiments were explicitly mentioned. |
| Software Dependencies | No | No specific software dependencies with version numbers were mentioned (e.g., library names with versions). |
| Experiment Setup | Yes | For our evaluation on smaller graphs, we generate random connected moral DAGs using the following procedure, which is a modification of Erdös-Rényi sampling that guarantees that the graph is connected. We first generate a random ordering σ over vertices. Then, for the n-th node in the order, we set its indegree to be Xn = max(1, Bin(n 1, ρ)), and sample Xn parents uniformly from the nodes earlier in the ordering. Finally, we chordalize the graph by running the elimination algorithm (Koller & Friedman, 2009) with elimination ordering equal to the reverse of σ. |