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].
Learning to search efficiently for causally near-optimal treatments
Authors: Samuel Håkansson, Viktor Lindblom, Omer Gottesman, Fredrik D. Johansson
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The methods are evaluated on synthetic and real-world healthcare data and compared to model-free reinforcement learning. We find that our methods compare favorably to the model-free baseline while offering a more transparent trade-off between search time and treatment efficacy. |
| Researcher Affiliation | Academia | Samuel H akansson University of Gothenburg EMAIL Viktor Lindblom Chalmers University of Technology EMAIL Omer Gottesman Brown University EMAIL Fredrik D. Johansson Chalmers University of Technology EMAIL |
| Pseudocode | No | The paper describes dynamic programming and greedy approximation algorithms but does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | Implementations can be found at: https://github.com/Healthy-AI/Treatment Exploration |
| Open Datasets | Yes | We evaluate our proposed methods using synthetic and real-world healthcare data... MIMIC-III database (Johnson et al., 2016). We consider training sets in a low-data regime with 50 samples and a high-data regime of 75000, with fixed test set size of 3000 samples. The cohort was split randomly into a training and test set with a 70/30 ratio and experiments were repeated over five such splits. |
| Dataset Splits | Yes | We consider training sets in a low-data regime with 50 samples and a high-data regime of 75000, with fixed test set size of 3000 samples. The cohort was split randomly into a training and test set with a 70/30 ratio and experiments were repeated over five such splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions methods like "function approximation using random forests" and "historical kernel-smoothing" but does not specify software dependencies with version numbers (e.g., Python version, library names and their versions). |
| Experiment Setup | Yes | Here, CDP and CG use δ = 0.4, = 0 and the upper bound of (10) and NDP λ = 0.35. We sweep all hyperparameters uniformly over 10 values; for CDP, CG, δ 2 [0, 1], for NDP H, λ 2 [0, 0.5] and for NDP F, λ 2 [0, 1]. |