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..
Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination
Authors: Kun Zhang, Biwei Huang, Jiji Zhang, Clark Glymour, Bernhard Schölkopf
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on various synthetic and real-world data sets are presented to demonstrate the efficacy of our methods. |
| Researcher Affiliation | Academia | Department of philosophy, Carnegie Mellon University MPI for Intelligent Systems, T ubingen, Germany Lingnan University, Hong Kong EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Detection of Changing Modules and Recovery of Causal Skeleton |
| Open Source Code | No | The paper does not provide an explicit statement about the release of source code for the described methodology or a direct link to a code repository. |
| Open Datasets | Yes | This f MRI Hippocampus dataset [Poldrack and Laumann, 2015] contains signals from six separate brain regions... The breast tumor dataset is from the UCI Machine Learning Depository [Blake and Merz, 1998]. |
| Dataset Splits | Yes | we trained SVM with these 11 features, subsets of these 11 features, random subsets of all features, and all 30 features, and used 10-fold cross-validation (CV) error to assess the classification accuracy. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for running experiments, such as CPU/GPU models or memory specifications. |
| Software Dependencies | No | The paper mentions using specific methods like 'kernel-based conditional independence test (KCI-test [Zhang et al., 2011])' and 'SGS search [Spirtes et al., 1993]', but does not provide specific version numbers for any software dependencies or libraries used in the implementation. |
| Experiment Setup | Yes | We tried different periods (w = 5, 10, 20, 30) on the time-varying component a, as well as different sample sizes (N = 600, 1000). In each setting, we ran 50 trials. ... We applied our enhanced constraint-based method on 10 successive days separately, with time information T as an additional variable in the system. ... we trained SVM with these 11 features, subsets of these 11 features, random subsets of all features, and all 30 features, and used 10-fold cross-validation (CV) error to assess the classification accuracy. |