Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings
Authors: Hengrui Cai, Chengchun Shi, Rui Song, Wenbin Lu
NeurIPS 2021 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our method is further justiļ¬ed by theoretical results, simulations, and a real application to Warfarin Dosing. |
| Researcher Affiliation | Academia | Hengrui Cai North Carolina State University Raleigh, USA EMAIL Chengchun Shi London School of Economics and Political Science London, UK EMAIL Rui Song North Carolina State University Raleigh, USA EMAIL Wenbin Lu North Carolina State University Raleigh, USA EMAIL |
| Pseudocode | Yes | We give the detailed pseudocode in Algorithm 1 in Appendix B due to page limit. |
| Open Source Code | Yes | The code is publicly available at our repository at https://github.com/Hengrui Cai/DJL. |
| Open Datasets | Yes | We use the dataset provided by the International Warfarin Pharmacogenetics [9] for analysis. ... [9] Consortium, I. W. P. [2009], Estimation of the warfarin dose with clinical and pharmacogenetic data , New England Journal of Medicine 360(8), 753 764. |
| Dataset Splits | No | The paper describes a data splitting and cross-fitting strategy but does not provide specific percentages, counts, or explicit predefined splits for training, validation, and testing of the main datasets used. |
| Hardware Specification | Yes | The computing infrastructure used is a virtual machine in the AWS Platform with 72 processor cores and 144GB memory. |
| Software Dependencies | No | The paper mentions using an 'MLP regressor implemented by Pedregosa et al. [36]' (referring to scikit-learn), but it does not specify version numbers for any software, libraries, or dependencies. |
| Experiment Setup | Yes | In our implementation, we set QI to the class of multilayer perceptrons (MLP) for each I. ... We set m = n/10 to achieve a good balance between the absolute error and the computational cost (see Figure 1 in Appendix C for details). |