A Class of Algorithms for General Instrumental Variable Models
Authors: Niki Kilbertus, Matt J. Kusner, Ricardo Silva
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experimental Results |
| Researcher Affiliation | Academia | Niki Kilbertus Helmholtz AI Matt J. Kusner University College London The Alan Turing Institute Ricardo Silva University College London The Alan Turing Institute |
| Pseudocode | Yes | Algorithm D in Appendix D describes the full procedure. |
| Open Source Code | Yes | 1Code available at https://github.com/nikikilbertus/general-iv-models. |
| Open Datasets | Yes | We now turn to a real dataset from a 1995/96 survey on family expenditure in the UK (Office for National Statistics, 2000). |
| Dataset Splits | No | The information is insufficient. The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, or test sets. |
| Hardware Specification | No | The information is insufficient. The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The information is insufficient. The paper mentions software such as JAX and scikit-learn, but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | All experiments use a single set of hyperparameters, see Appendix I.1. |