Causal Regularization
Authors: Dominik Janzing
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | 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 janzind@amazon.com |
| 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). |