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..
Comparison of meta-learners for estimating multi-valued treatment heterogeneous effects
Authors: Naoufal Acharki, Ramiro Lugo, Antoine Bertoncello, Josselin Garnier
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically confirm the strengths and weaknesses of those methods with synthetic and semi-synthetic datasets. |
| Researcher Affiliation | Collaboration | 1CMAP, Ecole polytechnique, Institut Polytechnique de Paris, Palaiseau, France 2Total Energies One Tech, Palaiseau, France. |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code and the semi-synthetic dataset in subsection 6.2 are available at https://github.com/nacharki/ multiple T-Meta Learners. |
| Open Datasets | Yes | The code and the semi-synthetic dataset in subsection 6.2 are available at https://github.com/nacharki/ multiple T-Meta Learners. |
| Dataset Splits | No | The paper mentions running experiments with "n = 2000 units" and "n = 10000 units" but does not specify explicit training, validation, or test dataset splits (e.g., percentages or exact sample counts for each split). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as CPU/GPU models, memory, or specific computing cluster configurations. |
| Software Dependencies | No | The paper mentions software components like "XGBoost model" and "Random Forest" as base-learners, but it does not provide specific version numbers for these or other software dependencies, which is necessary for reproducibility. |
| Experiment Setup | Yes | All hyperparameters (e.g. the number of trees, depth etc.) are fixed to their default values during all experiments. |