Confounding-Robust Policy Improvement
Authors: Nathan Kallus, Angela Zhou
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We assess our methods on synthetic data and a large clinical trial, demonstrating that confounded selection can hinder policy learning and lead to unwarranted harm, while our robust approach guarantees safety and focuses on well-evidenced improvement. |
| Researcher Affiliation | Academia | Nathan Kallus Cornell University and Cornell Tech New York, NY kallus@cornell.edu Angela Zhou Cornell University and Cornell Tech New York, NY az434@cornell.edu |
| Pseudocode | Yes | Algorithm 1: Parametric Subgradient Method |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or provide links to a code repository. |
| Open Datasets | Yes | We study the International Stroke Trial (IST)... [8] I. S. T. C. Group. The international stroke trial (ist): a randomised trial of aspirin, subcutaneous heparin, both, or neither among 19435 patients with acute ischaemic stroke. international stroke trial collaborative group. Lancet, 1997. |
| Dataset Splits | No | We construct an evaluation framework from the dataset by first sampling a split into a training set Strain and a held-out test set Stest, and subsampling a final set of initial patients, whose data is then used to train treatment assignment policies. |
| Hardware Specification | No | The paper does not specify any particular hardware used for conducting the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions methods like 'causal forests' and 'logistic regression' but does not list any specific software packages or their version numbers that were used for implementation or analysis. |
| Experiment Setup | No | The paper states 'For each of these we vary the parameter Γ in {0.3, 0.4, . . . , 1.6, 1.7, 2, 3, 4, 5}' and 'For the parametric policies, we optimize with the same parameters as earlier' referring to Algorithm 1's step size and step-schedule exponent. However, specific values for hyperparameters like learning rate, batch size, or optimizer settings are not provided. |