Differentially Private Nonlinear Causal Discovery from Numerical Data
Authors: Hao Zhang, Yewei Xia, Yixin Ren, Jihong Guan, Shuigeng Zhou
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive simulations and real-world experiments for both conditional independence test and causal discovery are conducted, which show that our method is effective in handling nonlinear numerical cases and easy to implement. |
| Researcher Affiliation | Academia | 1Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, China 2Department of Computer Science & Technology, Tongji University, China {haoz15, ywxia21, yxren21, sgzhou}@fudan.edu.cn; jhguan@tongji.edu.cn |
| Pseudocode | Yes | Algorithm 1: Differentially Private Nonlinear Causal Discovery (PCD) |
| Open Source Code | Yes | The source code of our method and data are available at https://github.com/Causality-Inference/PCD. |
| Open Datasets | Yes | The source code of our method and data are available at https://github.com/Causality-Inference/PCD. |
| Dataset Splits | No | The paper describes generating samples for experiments (e.g., "{200, 500} samples" and "{1000, 2000, 4000, 8000} samples") but does not provide specific training, validation, and test dataset splits with percentages or absolute counts for model evaluation. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using specific methods (e.g., KRR, HSIC), but it does not provide specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9) required to replicate the experiments. |
| Experiment Setup | Yes | We fix the regularization parameter λ = 1 for KRR, and set the kernel size to median distance between points and k = 100 permutations for HSIC. These are normal parameter settings for KRR and HSIC. |