Heavy-tailed max-linear structural equation models in networks with hidden nodes
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Recursive max-linear vectors provide models for the causal dependence between large values of observed random variables as they are supported on directed acyclic graphs (DAGs). But the standard assumption that all nodes of such a DAG are observed is often unrealistic. We provide necessary and sufficient conditions that allow for a partially observed vector from a regularly varying model to be represented as a recursive max-linear (sub-)model. Our method relies on regular variation and the minimal representation of a recursive max-linear vector. Here the max-weighted paths of a DAG play an essential role. Results are based on a scaling technique and causal dependence relations between pairs of nodes. In certain cases our method can also detect the presence of hidden confounders. Under a two-step thresholding procedure, we show consistency and asymptotic normality of the estimators. Finally, we study our method by simulation, and apply it to nutrition intake data.
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