Causal discovery with endogenous context variables
Authors: Wiebke Günther, Oana-Iuliana Popescu, Martin Rabel, Urmi Ninad, Andreas Gerhardus, Jakob Runge
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments demonstrate the performance of the proposed method over alternative baselines, but they also unveil current limitations of our method. [...] 5 Simulation study |
| Researcher Affiliation | Collaboration | 1German Aerospace Center (DLR), Institute of Data Science, Jena, Germany 2Technische Universität Berlin, Institute of Computer Engineering and Microelectronics, Berlin, Germany 3Center for Scalable Data Analytics and Artificial Intelligence (Sca DS.AI) Dresden / Leipzig, Germany 4Technische Universität Dresden, Faculty of Computer Science, Dresden, Germany |
| Pseudocode | Yes | Algorithm 1 Adaptive CD for Discovering Context-Specific Graphs with Endogenous Context Variables |
| Open Source Code | Yes | The code, based on causal-learn (40) and Tigramite (25), and details for replication (random seeds) can be found here: https://github.com/oanaipopescu/adaptive_endogenous_contexts. |
| Open Datasets | No | Our data models are created by first randomly generating a linear acyclic SCM with up to D + 1 nodes (base graph) of the form Xi = PD+1 j aij Xj + cjηj, where i = 1, . . . , D + 1, at a desired sparsity level s. [...] Once a context-specific SCM is created, we generate the data according to this SCM. The paper describes a process for generating synthetic data for each experiment, rather than using a fixed, publicly available dataset. |
| Dataset Splits | No | The paper focuses on generating synthetic data for its experiments and does not specify any fixed training, validation, or test dataset splits. Performance is evaluated based on metrics over the generated samples. |
| Hardware Specification | Yes | All results were obtained by conducting trials in parallel across a cluster of 116 CPU nodes, each equipped with 2x Intel Xeon Platinum 8260 processors. |
| Software Dependencies | No | The code, based on causal-learn (40) and Tigramite (25), and details for replication (random seeds) can be found here: https://github.com/oanaipopescu/adaptive_endogenous_contexts. The specific version numbers for causal-learn and Tigramite, or other software dependencies, are not provided in the paper. |
| Experiment Setup | Yes | We present results where D + 1 = 8 and density s = 0.4. For all j, ηj N(0, 1). We draw the non-zero ai,j randomly from {1.8, 1.5, 1.2, 1.2, 1.5, 1.8}, and set cj = 1 for all j. For the context indicator R, we set c R = 0.2. |