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..
Constrained Causal Bayesian Optimization
Authors: Virginia Aglietti, Alan Malek, Ira Ktena, Silvia Chiappa
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate c CBO on artificial and real-world causal graphs showing successful trade off between fast convergence and percentage of feasible interventions. |
| Researcher Affiliation | Industry | 1Deep Mind, London, UK. |
| Pseudocode | Yes | Algorithm 1 c CBO |
| Open Source Code | Yes | Code for reproducing the experiments is available at https://github.com/deepmind/ccbo. |
| Open Datasets | Yes | For the HEALTH graph, we use the SCM in Ferro et al. (2015)... For the PROTEIN-SIGNALING graph, we use the observational dataset from Sachs et al. (2005) |
| Dataset Splits | No | The paper does not explicitly mention train/validation/test splits, nor does it provide percentages or sample counts for such splits. It discusses observational and interventional datasets used during sequential experimentation. |
| Hardware Specification | No | No specific hardware details (e.g., CPU, GPU models, memory) are provided for the experimental setup. |
| Software Dependencies | No | No specific software dependencies with version numbers are mentioned in the paper. |
| Experiment Setup | Yes | For all experiments we initialize the kernel hyper-parameters (σ2 f, l) of the surrogate models to 1 and optimize them with a standard type-2 ML approach. When sampling from the modified SCM is required in order to compute the prior mean or kernel functions we use S = 10 samples. The same number of samples is used when the computation of the acquisition function requires a Monte Carlo approximation. |