Optimization over Continuous and Multi-dimensional Decisions with Observational Data
Authors: Dimitris Bertsimas, Christopher McCord
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the efficacy of our method on examples involving both synthetic and real data sets. |
| Researcher Affiliation | Academia | Dimitris Bertsimas Sloan School of Management Massachusetts Institute of Technology Cambridge, MA 02142 dbertsim@mit.edu Christopher Mc Cord Operations Research Center Massachusetts Institute of Technology Cambridge, MA 02142 mccord@mit.edu |
| Pseudocode | No | The paper describes algorithms and mathematical formulations but does not include any explicitly labeled pseudocode blocks or algorithm listings. |
| Open Source Code | No | The paper does not contain any statement about making its source code publicly available for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | data from Consortium et al. [10]. This publicly available data set contains the optimal stable dose, found by experimentation, for a diverse set of 5410 patients. |
| Dataset Splits | No | The paper states: 'For the examples in Section 4, we use a combination of these two ideas. We train a random forest model on the validation set (in order to impute counterfactual outcomes), and we then select the model that minimizes the sum of the mean squared error and the predicted cost on the validation data.' and 'To begin, we split the data into a training set of 4000 patients and a test set of 1410 patients.' While a validation set is mentioned for tuning, the specific size or split percentages for this validation set from the 4000 training patients are not explicitly provided, which would be needed for full reproducibility of the data partitioning. |
| Hardware Specification | No | The paper describes the methods and experimental setup, but it does not specify any hardware details such as GPU/CPU models, memory, or specific computing resources used for running the experiments. |
| Software Dependencies | No | The paper mentions machine learning algorithms like CART, random forest, Lasso, and xgboost but does not provide specific version numbers for any software libraries, programming languages, or solvers used in their implementation. |
| Experiment Setup | No | The paper describes general experimental procedures (e.g., averaging results over one hundred training sets, repeating work across randomizations), but it does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or other detailed training configurations that would be necessary to fully reproduce the experiments. |