Build a plan from this source.
[Re] Replication Study of DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks is included below. Adapt the question and comparison, then copy or download your plan.
Make the question your own.
1 source · Exact preparation context
- [Re] Replication Study of DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks →
Shulev, Velizar; Verhagen, Paul; Wang, Shuai; Zhuge, Jennifer. [Re] Replication Study of DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks. ReScience C 8(2), #43; 10.5281/zenodo.6574711.
Record revision: ad93011269a38d7e8dbc60848c87f706ceacec8a5a0e09e57a39592e5460ab2b
Source links open the current listing. This plan retains the revisions above; listing review does not grant permission to copy or adapt source files.
Before carrying out this plan
- PyTorch (named in journal metadata; dependency version not checked)
Limits to keep in view
- No research code was executed; no independent scientific verification has been performed.
- The current qualified pilot is self-contained Python 3.13 with a 90-second author deadline. This article's environment has not been qualified for that path.
- Published source metadata and a historical review do not establish compatibility, successful reproduction, operator independence or current scientific correctness.
- Dependencies named in metadata are not available under the current standard-library-only pilot; a new qualified environment is required.
Edit freely. Export a copy before reloading or leaving. Your text stays in this tab until you choose to copy, download or save it privately.
Need a starting point with sources? Explore the research briefs →