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..
An effective framework for estimating individualized treatment rules
Authors: Joowon Lee, Jared Huling, Guanhua Chen
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive simulations and applications demonstrate that our framework achieves significant gains in both robustness and effectiveness for ITR learning against existing methods. |
| Researcher Affiliation | Academia | 1 University of Wisconsin-Madison, 2 University of Minnesota EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Projected Gradient Descent Algorithm to Estimate Decision Function for ITR-Learning |
| Open Source Code | Yes | The code supporting this study is available at https://github.com/ljw9510/effective-ITR, with plans for release as an R package soon. |
| Open Datasets | Yes | We apply the proposed methods to two datasets from AIDS Clinical Trials Group (ACTG) 175 [21] and email marketing [22]. |
| Dataset Splits | Yes | Similar to [44], we randomly split the data into a training set of {200, 400, 800, 1000, 1200} observations for the ACTG dataset and {1000, 3000, 5000} observations for the email dataset. The remaining observations were used for test data with 10 iterations. |
| Hardware Specification | Yes | All numerical experiments were performed on a 2022 Macbook Air with M1 chip and 16 GB of RAM. |
| Software Dependencies | No | The paper mentions 'random Forest package in R' but does not specify version numbers for R or the package itself, which is required for a reproducible description of software dependencies. |
| Experiment Setup | Yes | Specifically, we use following treatment-free effect function ยต(X) and interaction effect function ฮด(X) for each scenario: 1. Randomized Trial: Linear ITR as the true optimal ยต(X) = 1 + 2X1 + 2X2, ฮด(X) = 0.75 + 1.5X1 + 1.5X2 + 1.5X3 + 1.5X4, A = 1; 0.75 + 1.5X1 1.5X2 1.5X3 + 1.5X4, A = 2; 0.75 + 1.5X1 1.5X2 + 1.5X3 1.5X4, A = 3; 0.75 1.5X1 + 1.5X2 1.5X3 1.5X4, A = 4, ... The iterate number T of the PGD algorithm is 1000. |