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..
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. |