A Dual-module Framework for Counterfactual Estimation over Time
Authors: Xin Wang, Shengfei Lyu, Lishan Yang, Yibing Zhan, Huanhuan Chen
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we validate the effectiveness of the proposed ACTIN through a series of experiments. Following the conventional workflow of counterfactual inference benchmarks (Melnychuk et al., 2022), we conduct comparative analyses of ACTIN against existing models on both simulated and real datasets. Subsequently, we examine in detail the running time and complexity of the baseline methods and ACTIN on different datasets. Ultimately, we experimentally explore the roles of different components in ACTIN. |
| Researcher Affiliation | Collaboration | 1School of Computer Science and Technology, University of Science and Technology of China, China 2Nanyang Technological University, Singapore 3JD Explore Academy. |
| Pseudocode | Yes | Algorithm 1 Pseudocode of Training ACTIN |
| Open Source Code | Yes | Code is available online: https://github.com/ waxin/ACTIN |
| Open Datasets | Yes | The Medical Information Mart for Intensive Care III (MIMIC-III) (Johnson et al., 2016) is a comprehensive database of electronic health records for patients in intensive care units, often used to evaluate the effectiveness of models in real and complex medical scenarios. [...] Mimic-iii, a freely accessible critical care database. Scientific data, 3(1):1 9, 2016. |
| Dataset Splits | Yes | This cohort was then distributed into training, validation, and testing datasets in a 70%/15%/15% proportion. [...] the dataset comprising 1,000 patients is partitioned into training, validation, and testing sets, adhering to a 60%/20%/20% distribution ratio. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions implementing ACTIN using the Pytorch Lightning framework and choosing the Adam algorithm for gradient optimization, but it does not specify version numbers for these software dependencies or any other key libraries. |
| Experiment Setup | Yes | we conduct hyperparameter optimization for all baseline models and ACTIN using random searches. The ranges for the random searches for RMSN, CRN, G-Net, and CT are provided in Tables 6, 7, 8, and 9, respectively. The random search space for ACTIN is outlined in Table 10. |