Achieving Counterfactual Fairness for Causal Bandit
Authors: Wen Huang, Lu Zhang, Xintao Wu6952-6959
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Both theoretical analysis and empirical evaluation demonstrate effectiveness of our algorithms. We conduct experiments on the Email Campaign data (Lu et al. 2020) whose results show the benefit of using the dseparation set from the causal graph. |
| Researcher Affiliation | Academia | Wen Huang, Lu Zhang, Xintao Wu University of Arkansas {wenhuang, lz006, xintaowu}@uark.edu |
| Pseudocode | Yes | Algorithm 1: D-UCB: Causal Bandit based on d-separation |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We adopt the Email Campaign data as used in previous works (Lu et al. 2020). We follow the settings of (Huang et al. 2020) by combining two publicly available datasets: Adult dataset and Youtube video dataset. |
| Dataset Splits | No | The paper describes using 'half of the data as the offline data to construct causal graph and adopt the other half to be user sequence and arm candidates for online recommendation' but does not specify explicit training, validation, and test splits with percentages or sample counts for model evaluation, nor does it mention cross-validation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as library names with version numbers (e.g., 'Python 3.8', 'PyTorch 1.9'). |
| Experiment Setup | Yes | In our experiment, we set δ = 1/t2 for each t [T]. The cumulative regret is added up through 5000 rounds. |