Contrastive Balancing Representation Learning for Heterogeneous Dose-Response Curves Estimation
Authors: Minqin Zhu, Anpeng Wu, Haoxuan Li, Ruoxuan Xiong, Bo Li, Xiaoqing Yang, Xuan Qin, Peng Zhen, Jiecheng Guo, Fei Wu, Kun Kuang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted on synthetic and real-world datasets demonstrating that our proposal significantly outperforms previous methods. Experiments Since the true HDRC are rarely available in real application, in line with previous work (Nie et al. 2021; Bica et al. 2020), we simulate 4 synthetic data and 5 semi-synthetic data from two real-world datasets IHDP2 and News3. |
| Researcher Affiliation | Collaboration | 1 Department of Computer Science and Technology, Zhejiang University 2Mohamed bin Zayed University of Artificial Intelligence 3Center for Data Science, Peking University 4Department of Quantitative Theory and Methods, Emory University 5 School of Economics and Management, Tsinghua University 6Didi Chuxing {minqinzhu, anpwu, kunkuang}@zju.edu.cn, hxli@stu.pku.edu.cn, ruoxuan.xiong@emory.edu, libo@sem.tsinghua.edu.cn, {xiaoqingyang, xuanqin, zhenpeng, jiechengguo}@didiglobal.com, wufei@cs.zju.edu.cn |
| Pseudocode | No | The paper includes figures and mathematical formulations but no explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | Yes | We simulate 4 synthetic data and 5 semi-synthetic data from two real-world datasets IHDP2 and News3. https://www.fredjo.com https://paperdatasets.s3.amazonaws.com/news.db |
| Dataset Splits | Yes | Then we sample 2100/600/300 units for training/validation/test for each data. We sample units from the IHDP data to create the training, validation, and test sets, with 522/150/75 units for each data split. For the News dataset, we perform data splits into training, validation, and test sets with 2100/600/300 units, respectively. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies, libraries, or frameworks used (e.g., 'PyTorch 1.x' or 'TensorFlow 2.x'). |
| Experiment Setup | No | The paper discusses tuning some hyperparameters like alpha and the dimension of representation K_Phi(X), and mentions setting 'm=1 with default'. However, it does not provide a comprehensive list of all key hyperparameters (e.g., learning rate, batch size, optimizer, number of epochs) or other system-level training settings used for the main experimental results. |