Notice: 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. Full accuracy metrics and methodology are described in [1].
A Generative Adversarial Framework for Bounding Confounded Causal Effects
Authors: Yaowei Hu, Yongkai Wu, Lu Zhang, Xintao Wu12104-12112
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments using both synthetic and real-world datasets are conducted. |
| Researcher Affiliation | Academia | 1 University of Arkansas 2 Clemson University EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | The pseudocode of above procedure is given in Algorithm 1 in the supplementary ο¬le. |
| Open Source Code | Yes | Reproducibility. The source code is available at https://github.com/yaoweihu/Bound-Confounded-Causal-Effects. |
| Open Datasets | Yes | We use both synthetic data and a real-world dataset, Adult (Dheeru and Karra Taniskidou 2017). |
| Dataset Splits | No | The paper does not provide specific training, validation, or test set splits (e.g., percentages or counts) for the datasets used in the experiments. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU models, CPU types, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like "Wasserstein GAN" and "adaptive gradient clipping" but does not provide specific version numbers for any libraries, frameworks, or programming languages. |
| Experiment Setup | Yes | In the implementation of our method, we use one hidden layer with 16 nodes for all generators GV ( ) and neural networks h V ( ; ), and use Re LU as the activation function. [...] The threshold Ξ· in the constraint is set to 0.001, and we take 50 solutions satisfying the constraint to compute the mean and variance. The upper bound is computed as mean + std, and the lower bound is computed as mean std. The training procedure is as follows. We continually sample mini-batches of noise samples z. |