Work

Local Independence Testing for Point Processes

Year: 2023

Type: article

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.

Source: IEEE Transactions on Neural Networks and Learning Systems

DOI: 10.1109/tnnls.2023.3335265

Authors: Nikolaj Thams, Niels Richard Hansen

Institution: University of Copenhagen

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Villum Fonden, Novo Nordisk, Novo Nordisk Fonden

Awards: G3211459053, G711721537