Representation Learning for Treatment Effect Estimation from Observational Data
Authors: Liuyi Yao, Sheng Li, Yaliang Li, Mengdi Huai, Jing Gao, Aidong Zhang
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on synthetic and three real-world datasets demonstrate the advantages of the proposed SITE method, compared with the state-of-the-art ITE estimation methods. |
| Researcher Affiliation | Collaboration | Liuyi Yao SUNY at Buffalo liuyiyao@buffalo.edu Sheng Li University of Georgia sheng.li@uga.edu Yaliang Li Tencent Medical AI Lab yaliangli@tencent.com Mengdi Huai SUNY at Buffalo mengdihu@buffalo.edu Jing Gao SUNY at Buffalo jing@buffalo.edu Aidong Zhang SUNY at Buffalo azhang@buffalo.edu |
| Pseudocode | No | The paper describes the proposed method using prose, mathematical equations, and figures, but it does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code of SITE is available at https://github.com/Osier-Yi/SITE. |
| Open Datasets | Yes | IHDP and Jobs dataset are adopted in [30]... The twins dataset comes from the all twins birth in the USA between 1989 1991 [2]. |
| Dataset Splits | No | The paper mentions 'training dataset' and 'test dataset' but does not provide specific percentages, sample counts, or detailed methodologies for train/validation/test splits, nor does it explicitly mention a separate 'validation' set or how the splits were performed (e.g., random seed, stratified). |
| Hardware Specification | Yes | Also, we gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. |
| Software Dependencies | No | The paper mentions general software components like 'Adam' (optimizer), 'Dropout', and 'Re LU activation function' but does not provide specific version numbers for any libraries, frameworks, or programming languages used (e.g., TensorFlow 2.x, PyTorch 1.x, Python 3.x). |
| Experiment Setup | No | The paper describes the model architecture (feed-forward neural networks with dh hidden layers, ReLU activation) and the optimizer (Adam). However, it does not provide specific hyperparameter values such as learning rate, batch size, number of epochs, or the exact number of hidden layers (dh is a variable, not a concrete number). |