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].
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. |