Multiply Robust Off-policy Evaluation and Learning under Truncation by Death
Authors: Jianing Chu, Shu Yang, Wenbin Lu
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conduct experiments to demonstrate the empirical performance of the proposed estimators. |
| Researcher Affiliation | Academia | 1Department of Statistics, North Carolina State University, Raleigh, NC, USA. Correspondence to: Jianing Chu <jchu3@ncsu.edu>. |
| Pseudocode | Yes | Algorithm 1 Multiply Robust OPE with Cross-fitting |
| Open Source Code | No | The paper does not provide any statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We illustrate the proposed methods using an application to data from the MIMIC-III clinical database (Goldberger et al., 2000; Johnson et al., 2016; 2019). |
| Dataset Splits | No | We randomly sample the training data with a size 798 × 50% = 399 and the remaining sample is used for testing. There is no explicit mention of a separate validation split or cross-validation being used for model selection/tuning. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory, cloud instances) used to run the experiments. |
| Software Dependencies | No | The paper mentions software components like "logistic regression models", "log-linear regression model", "generalized additive models (GAMs)", "random forest (RF) model", and "genetic algorithm" but does not specify their version numbers. |
| Experiment Setup | No | The paper describes the types of models used for nuisance functions and mentions the genetic algorithm for optimization, but it does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, number of epochs) or specific configurations for the models used. |