A Generative Adversarial Framework for Bounding Confounded Causal Effects
Authors: Yaowei Hu, Yongkai Wu, Lu Zhang, Xintao Wu12104-12112
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | 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 yaoweihu@uark.edu, yongkaw@clemson.edu, lz006@uark.edu, xintaowu@uark.edu |
| Pseudocode | Yes | The pseudocode of above procedure is given in Algorithm 1 in the supplementary file. |
| 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 η 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. |