Estimation of Bounds on Potential Outcomes For Decision Making
Authors: Maggie Makar, Fredrik Johansson, John Guttag, David Sontag
ICML 2020 | Conference PDF | Archive PDF | Plain Text | 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 <mmakar@mit.edu>. |
| 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. |