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 | Conference PDF | Archive PDF | Plain Text | 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 csquires@mit.edu Sara Magliacane MIT-IBM Watson AI Lab IBM Research sara.magliacane@gmail.com Kristjan Greenewald MIT-IBM Watson AI Lab IBM Research kristjan.h.greenewald@ibm.com Dmitriy Katz MIT-IBM Watson AI Lab IBM Research dkatzrog@us.ibm.com Murat Kocaoglu MIT-IBM Watson AI Lab IBM Research murat@ibm.com Karthikeyan Shanmugam MIT-IBM Watson AI Lab IBM Research karthikeyan.shanmugam2@ibm.com |
| 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. |