Local Independence Testing for Point Processes
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Constraint-based causal structure learning for point processes require empirical tests of local independence. Existing tests require strong model assumptions, e.g., that the true data generating model is a Hawkes process with no latent confounders. Even when restricting attention to Hawkes processes, latent confounders are a major technical difficulty because a marginalized process will generally not be a Hawkes process itself. We introduce an expansion similar to Volterra expansions as a tool to represent marginalized intensities. Our main theoretical result is that such expansions can approximate the true marginalized intensity arbitrarily well. Based on this, we propose a test of local independence and investigate its properties in real and simulated data.
Originalsprog | Engelsk |
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Tidsskrift | IEEE Transactions on Neural Networks and Learning Systems |
Sider (fra-til) | 1-12 |
Antal sider | 12 |
ISSN | 2162-237X |
DOI | |
Status | E-pub ahead of print - 2024 |
Bibliografisk note
Publisher Copyright:
IEEE
ID: 384911241