Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Treatment Effect Estimation with Disentangled Latent Factors
Authors: Weijia Zhang, Lin Liu, Jiuyong Li10923-10930
AAAI 2021 | Venue PDF | LLM Run Details | Input Tokens: 17,464 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,710 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL,EMAIL |
| 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. |