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..
Estimation of Bounds on Potential Outcomes For Decision Making
Authors: Maggie Makar, Fredrik Johansson, John Guttag, David Sontag
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our algorithm on a semi-synthetic clinical dataset and a well-known causality benchmark. We show how it can guide treatment decisions, and that it achieves a better trade-off between bound violations and utility than baseline algorithms. |
| Researcher Affiliation | Academia | 1CSAIL, MIT 2Chalmers University of Technology. Correspondence to: Maggie Makar <EMAIL>. |
| Pseudocode | Yes | The procedure is summarized in Algorithm 1 in the supplement. |
| Open Source Code | Yes | Our code is available at <github.com/mymakar/bpo.git>. |
| Open Datasets | Yes | We use data from a randomized control trial measuring the effects of Heparin (International Stroke Trial Collaborative Group, 1997). |
| Dataset Splits | Yes | In each simulation, we randomly sample 3000 patients for training and validation and 3000 held out for testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions software components like 'RBF kernel', 'kernel regression', 'Gaussian processes', and 'logistic regression', but does not provide specific version numbers for any of these software dependencies. |
| Experiment Setup | Yes | We pick the regularization parameter for the propensity score model and all the response surface models via 3-fold cross-validation as described in detail in the supplement. |