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..
Towards Safe Policy Learning under Partial Identifiability: A Causal Approach
Authors: Shalmali Joshi, Junzhe Zhang, Elias Bareinboim
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments We evaluate the proposed method on 1) Synthetic data, and 2) the International Stroke Trial (IST) data (Group et al. 1997; Sandercock, Niewada, and Członkowska 2011) and learn four policies. |
| Researcher Affiliation | Academia | 1Department of Biomedical Informatics 2Department of Computer Science Columbia University New York, NY |
| Pseudocode | Yes | Algorithm 1: Safe Policy Learning |
| Open Source Code | No | The paper does not explicitly state that the source code for the methodology is available or provide a link. |
| Open Datasets | Yes | International Stroke Trial (IST) data (Group et al. 1997; Sandercock, Niewada, and Członkowska 2011) |
| Dataset Splits | Yes | Fig. 3 shows the mean outcome for varying thresholds averaged over 5-fold cross-validation (standard errors not visible due to low variability). Also: We set aside 30% data as a held-out test set. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models or types of processors used for experiments. |
| Software Dependencies | No | The paper mentions using a Multi-layer Perceptron (MLP) and GELU activations, but does not specify software versions for libraries, frameworks, or programming languages. |
| Experiment Setup | Yes | The function family Π (see Eq. (26)) corresponds to a two-layer Multi-layer Perceptron (MLP) with 5 hidden units and the GELU activations (Hendrycks and Gimpel 2016). Also from Algorithm 1: 'Input: ... learning rate λ > 0'. |