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..
Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environments
Authors: Jonas Schweisthal, Dennis Frauen, Mihaela Van Der Schaar, Stefan Feuerriegel
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We further demonstrate the effectiveness of our meta-learners across various experiments using both simulated and real-world data. |
| Researcher Affiliation | Academia | 1LMU Munich, Germany 2Munich Center for Machine Learning (MCML), Germany 3University of Cambridge, UK. |
| Pseudocode | Yes | Algorithm 1: Two-stage learners for estimating bounds |
| Open Source Code | Yes | Code is available at https://github.com/JSchweisthal/Bound Meta Learners. |
| Open Datasets | Yes | Here, we perform a case study using a dataset with COVID-19 hospitalizations in Brazil across different regions (Baqui et al., 2020). |
| Dataset Splits | Yes | To create the simulated data used in Sec. 6, for both datasets, we sample n = 10000 from the data-generating process above. We then split the data into train (70%), val (10%), and test (20%) sets. |
| Hardware Specification | No | The paper does not specify the exact hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using a 'software package https://github.com/Alicia Curth/CATENets' and 'Py Torch CATE meta-learners' but does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | Here, the networks for the first- and second-stage models are simple MLPs with 2 hidden layers and hidden neuron size of 100. |