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About this paper
Gabriele Visentin, Patrick Cheridito
The authors introduce a graph-aware normalizing flow for generating data that preserves supplied causal relationships. It measures distributional closeness with a graph-constrained Wasserstein distance and proves continuity of optimization values under stated conditions. In simulated causal regression, its augmentation performs better in worst-case metrics than RealNVP and VAE as tested intervention strength increases. The results do not establish real-world validity.
Summary by alphaXiv.
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