Treatment Effect Estimation with Disentangled Latent Factors
Authors: Weijia Zhang, Lin Liu, Jiuyong Li10923-10930
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate the effectiveness of the proposed method on a wide range of synthetic, benchmark, and real-world datasets. |
| Researcher Affiliation | Academia | Weijia Zhang*, Lin Liu, Jiuyong Li University of South Australia weijia.zhang.xh@gmail.com,{lin.liu,jiuyong.li}@unia.edu.au |
| Pseudocode | No | No, the paper describes the model and inference steps in text and equations but does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | The code is available at https://github.com/Weijia Zhang24/TEDVAE. |
| Open Datasets | Yes | The 2016 Atlantic Causal Inference Challenge (ACIC2016) (Dorie et al. 2019)... This dataset can be accessed at https: //github.com/vdorie/aciccomp/tree/master/2016. ... The Infant Health and Development Program (IHDP)... The datasets can be accessed at https: //github.com/vdorie/npci. |
| Dataset Splits | Yes | with a 60%/30%/10% train/validation/test splitting proportions. |
| Hardware Specification | No | No, the paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. It only mentions general aspects of the neural network architecture like number of layers and neurons. |
| Software Dependencies | No | No, the paper does not specify any software dependencies with version numbers (e.g., 'PyTorch 1.9', 'Python 3.8'). |
| Experiment Setup | Yes | As a result, we set the latent dimensionality parameters as Dzy = 5, Dzt = 15, Dzc = 15 and set the weight for auxiliary losses as αt = αy = 100. For all the parametrized neural networks, we use 5 hidden layers and 100 hidden neurons in each layer, with ELU activation. with a 60%/30%/10% train/validation/test splitting proportions. |