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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Confounding-Robust Policy Improvement
Authors: Nathan Kallus, Angela Zhou
NeurIPS 2018 | Venue PDF | 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 EMAIL Angela Zhou Cornell University and Cornell Tech New York, NY EMAIL |
| 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 ๏ฌrst sampling a split into a training set Strain and a held-out test set Stest, and subsampling a ๏ฌnal 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 ฮ 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. |