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].
Adaptively Exploiting d-Separators with Causal Bandits
Authors: Blair Bilodeau, Linbo Wang, Dan Roy
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we observe these performance improvements on simulated data. |
| Researcher Affiliation | Academia | Blair Bilodeau University of Toronto Linbo Wang University of Toronto Daniel M. Roy University of Toronto |
| Pseudocode | Yes | Algorithm 1: HAC-UCB(A, Z, T, (Z)) |
| Open Source Code | Yes | Implementation details are available in Appendix D and code can be found at https://github.com/blairbilodeau/adaptive-causal-bandits. |
| Open Datasets | No | The paper uses 'simulated data' (Section 5). It does not provide concrete access information (link, DOI, repository, or citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper describes simulated environments but 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 paper states: 'All computations were done on CPU on a personal laptop computer.' This is not specific enough as it does not include exact CPU models, processor types, or memory details. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers. |
| Experiment Setup | Yes | The paper describes the setup for its simulations in Section 5, including: 'Taking the gap = |A| (log T)/T, the fixed conditional distribution for Z = {0, 1} is Y | Z Ber(1/2 + (1 Z) ). Then, for a small Á (we take Á = 0.0005), we set P 1[Z = 0] = 1 Á and P a[Z = 0] = Á for all other a œ A \ {1}.' |