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 [1].
BayesIMP: Uncertainty Quantification for Causal Data Fusion
Authors: Siu Lun Chau, Jean-Francois Ton, Javier González, Yee Teh, Dino Sejdinovic
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To demonstrate the utility of our uncertainty estimation, we apply our method to the Causal Bayesian Optimisation task and show improvements over state-of-the-art methods. |
| Researcher Affiliation | Collaboration | Siu Lun Chau University of Oxford Jean-François Ton University of Oxford Javier González Microsoft Research Cambridge Yee Whye Teh University of Oxford Dino Sejdinovic University of Oxford |
| Pseudocode | No | The paper describes algorithms verbally and mathematically but does not include a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not contain any explicit statement about open-sourcing the code or a link to a code repository. |
| Open Datasets | Yes | On one hand we have observational data, from one medical study D1 [6], describing the causal relationship between statin level and prostate specific antigen (PSA), and on the other hand we have observational data, from a second study D2 [7], that looked into the link between PSA level and prostate cancer volume. |
| Dataset Splits | No | The paper mentions "N = 100 datapoints for D1 and M = 50 datapoints D2." and "In Fig.5 we plot the mean and the 95% credible interval of the resulting GP models for E[T|do(X) = x]." However, it does not explicitly provide training/validation/test splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., 'Python 3.8', 'PyTorch 1.9'). |
| Experiment Setup | No | The paper mentions general experimental settings like using the 'expected improvement (EI) acquisition function' and that 'The data generation and details were added in the Appendix', but it does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed training configurations in the main text. |