Local Independence Testing for Point Processes

Research output: Contribution to journalJournal articleResearchpeer-review

1 Citation (Scopus)
17 Downloads (Pure)

Abstract

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.

Original languageEnglish
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume35
Issue number4
Pages (from-to)4902-4910
ISSN2162-237X
DOIs
Publication statusPublished - 2024

Bibliographical note

Publisher Copyright:
IEEE

Keywords

  • Causal discovery
  • Data models
  • Heuristic algorithms
  • Kernel
  • Learning systems
  • local independence
  • Mathematical models
  • Neurons
  • neuroscience
  • point processes
  • Testing

Cite this