Local Independence Testing for Point Processes
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Local Independence Testing for Point Processes. / Thams, Nikolaj; Hansen, Niels Richard.
I: IEEE Transactions on Neural Networks and Learning Systems, Bind 35, Nr. 4, 2024, s. 4902-4910.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Local Independence Testing for Point Processes
AU - Thams, Nikolaj
AU - Hansen, Niels Richard
N1 - Publisher Copyright: IEEE
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Causal discovery
KW - Data models
KW - Heuristic algorithms
KW - Kernel
KW - Learning systems
KW - local independence
KW - Mathematical models
KW - Neurons
KW - neuroscience
KW - point processes
KW - Testing
U2 - 10.1109/TNNLS.2023.3335265
DO - 10.1109/TNNLS.2023.3335265
M3 - Journal article
C2 - 38109252
AN - SCOPUS:85181554691
VL - 35
SP - 4902
EP - 4910
JO - IEEE Transactions on Neural Networks and Learning Systems
JF - IEEE Transactions on Neural Networks and Learning Systems
SN - 2162-237X
IS - 4
ER -
ID: 384911241