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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Position: The Causal Revolution Needs Scientific Pragmatism
Authors: Joshua R. Loftus
ICML 2024 | Venue PDF | LLM Run Details | Input Tokens: 16,049 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,059 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Position: The Causal Revolution Needs Scientific Pragmatism |
| Researcher Affiliation | Academia | 1Department of Statistics, London School of Economics, London, UK. Correspondence to: Joshua Loftus <EMAIL>. |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide explicit access to source code for its own methodology as it is a position paper. |
| Open Datasets | No | The paper does not describe the use of any dataset for training or provide access information for one. |
| Dataset Splits | No | The paper does not describe any dataset splits for validation or other purposes. |
| Hardware Specification | No | The paper does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper does not describe any experimental setup details such as hyperparameters. |