ArticleEntropy (Basel, Switzerland)2021
Causal Discovery in High-Dimensional Point Process Networks with Hidden Nodes.
Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- A Sequential Markov Probabilistic Aggregation Algorithm for Causal Emergence in Markov Aggregation.Entropy (Basel, Switzerland) · 2026Article
- Stimulus type shapes the topology of cellular functional networks in mouse visual cortex.Nature communications · 2024Article
- Causal Inference for Heterogeneous Data and Information Theory.Entropy (Basel, Switzerland) · 2023Article
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Authors and funding
2 authors.
Funding
Abstract
Thanks to technological advances leading to near-continuous time observations, emerging multivariate point process data offer new opportunities for causal discovery. However, a key obstacle in achieving this goal is that many relevant processes may not be observed in practice. Naïve estimation approaches that ignore these hidden variables can generate misleading results because of the unadjusted confounding. To plug this gap, we propose a deconfounding procedure to estimate high-dimensional point process networks with only a subset of the nodes being observed. Our method allows flexible connections between the observed and unobserved processes. It also allows the number of unobserved processes to be unknown and potentially larger than the number of observed nodes. Theoretical analyses and numerical studies highlight the advantages of the proposed method in identifying causal interactions among the observed processes.
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Registered trials
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