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 [1].
Learning Nonparametric Latent Causal Graphs with Unknown Interventions
Authors: Yibo Jiang, Bryon Aragam
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we verify our theoretical results in a simulation study. ... We test the theoretical results on simulated datasets under two settings: pure child and single latent source. ... The results show that even without the graphical assumptions, our method can be effective in recovering the DAG for nonlinear models. |
| Researcher Affiliation | Academia | Yibo Jiang University of Chicago EMAIL Bryon Aragam University of Chicago EMAIL |
| Pseudocode | Yes | Pseudocode for the overall approach can be found in Algorithm 2 in Appendix G. |
| Open Source Code | No | The paper does not provide an explicit statement or a link to open-source code for the methodology described. |
| Open Datasets | No | The paper states, "We test the theoretical results on simulated datasets" but does not provide access information (link, citation, or repository) for a publicly available dataset. |
| Dataset Splits | No | The paper states "We test the theoretical results on simulated datasets" and mentions running "100 runs" but does not specify training, validation, or test splits. The term "validation" is used in the schema but not in the context of dataset splits in the paper. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments. |
| Software Dependencies | No | The paper mentions "Chatterjee’s coefficient" but does not list any software components or libraries with specific version numbers. |
| Experiment Setup | Yes | For each variable Vi in the causal graph, the structural equation is simply Vi P Vj pa(Vi) f(Vj) + ϵ, where ϵ is Gaussian noise, and f is a nonlinear function. We set f to be a quadratic function. To test independence, we adopt Chatterjee’s coefficient [10]. |