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].
Discovering Temporal Causal Relations from Subsampled Data
Authors: Mingming Gong, Kun Zhang, Bernhard Schoelkopf, Dacheng Tao, Philipp Geiger
ICML 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on both simulated and real data are reported to illustrate the performance of the proposed approaches. |
| Researcher Affiliation | Academia | 1 Centre for Quantum Computation and Intelligent Systems, FEIT, University of Technology, Sydney, NSW, Australia 2 Max Plank Institute for Intelligent Systems, T ubingen 72076, Germany 3 Information Sciences Institute, University of Southern California |
| Pseudocode | No | No pseudocode or algorithm blocks were found. The methods are described in narrative text. |
| Open Source Code | No | No statement or link providing access to open source code for the methodology. |
| Open Datasets | Yes | We conducted experiments on the Temperature Ozone data and the Temperature in House data (Peters et al., 2013). The Temperature Ozone data is the 50th causal-effect pair from the website https://webdav.tuebingen.mpg.de/cause-effect/. |
| Dataset Splits | Yes | In our experiments, we used 5-fold cross validation. |
| Hardware Specification | No | No specific hardware details (like CPU, GPU models, or memory) were mentioned for running experiments. |
| Software Dependencies | No | No specific software dependencies with version numbers were mentioned. |
| Experiment Setup | Yes | The elements in A are uniformly distributed between 0.5 and 0.5. The Gaussian mixture model contains two components for each dimension. We used both super-Gaussian and sub-Gaussian distributions for the noise terms. The parameters were wi,1 = 0.8, wi,2 = 0.2, µi,1 = 0, µi,2 = 0, σi,1 = 0.05, σi,2 = 1 for super-Gaussian noise and wi,1 = 0.5, wi,2 = 0.5, µi,1 = 2, σi,2 = 2, σi,1 = 0.5, σi,2 = 0.5 for sub-Gaussian noise. |