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..
Causal Regularization
Authors: Dominik Janzing
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Section 4 describes some empirical results. and 4 Experiments |
| Researcher Affiliation | Industry | Dominik Janzing Amazon Research Tübingen Germany EMAIL |
| Pseudocode | Yes | Our confounder correction algorithm reads: Algorithm Con Corr |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | Yes | Taste of wine This data has been extracted from the UCI machine learning repository [22] for the experiments in [14]. The cause X contains 11 ingredients of different sorts of red wine and Y is the taste assigned by human subjects. and [22] D. Dua and C. Graff. UCI machine learning repository, 2017. http://archive.ics.uci. edu/ml. |
| Dataset Splits | Yes | We have used leave-one-out CV from the Python package scikit for Ridge and Lasso, respectively. |
| Hardware Specification | No | No specific hardware details (such as GPU/CPU models, memory, or cloud instances) used for running the experiments are mentioned in the paper. |
| Software Dependencies | No | The paper mentions 'Python package scikit' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | For some fixed values of d = ℓ= 30, we generate one mixing matrix M in each run by drawing its entries from the standard normal distribution. In each run we generate n = 1000 instances of the ℓ-dimensional standard normal random vector Z and compute the X values by X = ZM. Afterwards we draw the entries of c and a from N(0, σ2 c) and N(0, σ2 a), respectively, after choosing σa and σc from the uniform distribution on [0, 1]. Finally, we compute the values of Y via Y = Xa + Zc + E, where E is random noise drawn from N(0, σ2 E) (the parameter σE has previously been chosen uniformly at random from [0, 5], which yields quite noisy data). |