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Local Independence Testing for Point Processes
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DOI：10.1109/TNNLS.2023.3335265.png)
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摘要
En 中文
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.
Keyword:
Causal discovery
local independence
neuroscience
point processes
期刊
IF:
8.9
论文数:
7.6K
被引数:
7.2W
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引用论文
COUNTERFACTUAL ANALYSES WITH GRAPHICAL MODELS BASED ON LOCAL INDEPENDENCE.png)
ANNALS OF STATISTICS
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ANNALS OF STATISTICSIF3.7
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