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..
Towards practical differentially private causal graph discovery
Authors: Lun Wang, Qi Pang, Dawn Song
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated Priv-PC on 7 public datasets and compared with the state-of-the-art. The results show that Priv-PC achieves 10.61 to 293.87 times speedup and better utility. |
| Researcher Affiliation | Academia | Lun Wang University of California, Berkeley EMAIL Qi Pang Zhejiang University EMAIL Dawn Song University of California, Berkeley EMAIL |
| Pseudocode | Yes | Algorithm 1: One-off sieve-and-examine mechanism. Algorithm 2: Priv-PC Algorithm with Kendall s τ. |
| Open Source Code | Yes | The implementation of Priv-PC, including the code used in our evaluation, is available at https://github.com/sunblaze-ucb/ Priv-PC-Differentially-Private-Causal-Graph-Discovery. |
| Open Datasets | Yes | We evaluated Priv-PC on 7 public datasets... The detailed information about the datasets is shown in Table 1. Dataset Type: Earthquake [15], Cancer [15], Asia [18], Survey [27], Alarm [3], Sachs [26], Child [4]. |
| Dataset Splits | No | The paper does not provide explicit training/test/validation dataset splits, such as percentages or sample counts for each split. |
| Hardware Specification | Yes | All the experiments were run on a Ubuntu18.04 LTS server with 32 AMD Opteron(TM) Processor 6212 with 512GB RAM. |
| Software Dependencies | No | The paper mentions running experiments on "Ubuntu18.04 LTS" but does not specify versions for any other ancillary software, libraries, or programming languages. |
| Experiment Setup | Yes | To directly compare EM-PC and Priv-PC, we ran the two algorithms on the datasets with 21 different privacy parameters and presented the results with accumulated privacy cost between 1 and 100. Furthermore, to demonstrate the utility improvement due to sieve-and-examine, we also directly applied sparse vector technique to PC algorithm (SVT-PC) and evaluated it under the same setting. For each privacy parameter, we ran the three algorithms for 5 times and recorded the mean and standard deviation of the utility of the output graph and the running time. We fix δ = 1e-3 for both EM-PC and Priv-PC across all the experiments. |