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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Differentially Private Nonlinear Causal Discovery from Numerical Data
Authors: Hao Zhang, Yewei Xia, Yixin Ren, Jihong Guan, Shuigeng Zhou
AAAI 2023 | Venue PDF | 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 EMAIL; EMAIL |
| 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. |