Detecting non-causal artifacts in multivariate linear regression models
Authors: Dominik Janzing, Bernhard Schölkopf
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 7. Experiments with simulated data |
| Researcher Affiliation | Collaboration | 1Amazon Development Center, T ubingen, Germany 2Max Planck Institute for Intelligent Systems, T ubingen, Germany. |
| Pseudocode | No | The paper describes methods conceptually and mathematically but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The data sets and the code are available at http://webdav. tuebingen.mpg.de/causality/ |
| Open Datasets | Yes | This dataset (Lichman, 2013) describes the dependence between the scores on the taste between 0 and 10 (given by human subjects) of red wine, and 11 different ingredients: and The data set is available at http://research.ics.aalto.fi/ica/eegmeg/MEG_data.html |
| Dataset Splits | No | The paper does not provide specific train/validation/test splits or cross-validation details for its experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency versions (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We have estimated β as described at the end of section 4 for d = ℓ= 10, 20, 50, 100 with sample size 10, 000. |