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..
Detecting non-causal artifacts in multivariate linear regression models
Authors: Dominik Janzing, Bernhard Schölkopf
ICML 2018 | Venue PDF | 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. |