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..
Identifying Representations for Intervention Extrapolation
Authors: Sorawit Saengkyongam, Elan Rosenfeld, Pradeep Kumar Ravikumar, Niklas Pfister, Jonas Peters
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our theoretical findings through a series of synthetic experiments and show that our approach can indeed succeed in predicting the effects of unseen interventions. and 6 EXPERIMENTS We now conduct simulation experiments to empirically validate our theoretical findings. |
| Researcher Affiliation | Academia | 1ETH Zürich 2Carnegie Mellon University 3University of Copenhagen |
| Pseudocode | Yes | Algorithm 1: An algorithm for Rep4Ex |
| Open Source Code | Yes | The code for all experiments is included in the supplementary material. |
| Open Datasets | No | The paper uses data generated from defined Structural Causal Models (SCMs) for its experiments (e.g., S(α) and S(γ)), which are synthetic. No concrete access information or citation to a public dataset is provided. |
| Dataset Splits | No | The paper mentions 'training support' and generating '100 test points' for some experiments, but does not provide specific train/validation/test split percentages or sample counts for the main experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like 'neural networks', 'Adam optimizer', and 'Leaky ReLU activation functions', but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Learning rate: 0.005 Batch size: 256 Optimizer: Adam optimizer with β1 = 0.9, β2 = 0.999 Number of epochs: 1000. and Architecture: three hidden layers with the hidden size of 32 |