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].
Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification
Authors: Hyun-Suk Lee, Yao Zhang, William Zame, Cong Shen, Jang-Won Lee, Mihaela van der Schaar
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments using synthetic and semi-synthetic datasets (based on real-world data) demonstrate that R2P outperforms state-of-the-art methods by more robustly identifying subgroups while providing much narrower confidence intervals. |
| Researcher Affiliation | Academia | Hyun-Suk Lee Sejong University EMAIL Yao Zhang University of Cambridge EMAIL William R. Zame UCLA EMAIL Cong Shen University of Virginia EMAIL Jang-Won Lee Yonsei University EMAIL Mihaela van der Schaar University of Cambridge UCLA The Alan Turing Institute EMAIL |
| Pseudocode | Yes | Algorithm 1 Robust Recursive Partitioning |
| Open Source Code | Yes | The code of R2P is available at: https://bitbucket.org/mvdschaar/mlforhealthlabpub. |
| Open Datasets | Yes | The two semi-synthetic datasets are based on real world data; the first uses the Infant Health and Development Program (IHDP) dataset [24] and the second uses the Collaborative Perinatal Project (CPP) dataset [25]. |
| Dataset Splits | No | The paper describes internal splitting for the SCR method (training set I1 and validation set I2) and for subgroup partitioning, but does not provide specific train/validation/test dataset splits for the overall experimental evaluation on the named datasets. |
| Hardware Specification | No | The paper does not provide any specific hardware specifications (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components and methods like 'causal multi-task Gaussian process (CMGP)', 'Random Forest', 'multi-task Gaussian processes', and 'deep neural networks', but does not provide specific version numbers for any software or libraries used in the experiments. |
| Experiment Setup | Yes | We set the miscoverage rate to be α = 0.05, so we demand a 95% ITE coverage rate. |