An effective framework for estimating individualized treatment rules
Authors: Joowon Lee, Jared Huling, Guanhua Chen
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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 joowon.lee@wisc.edu, huling@umn.edu, gchen25@wisc.edu |
| 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 µ(X) and interaction effect function δ(X) for each scenario: 1. Randomized Trial: Linear ITR as the true optimal µ(X) = 1 + 2X1 + 2X2, δ(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. |