ArticleJournal of the Royal Statistical Society. Series B, Statistical methodology2022
Functional Structural Equation Model.
Article in Journal of the Royal Statistical Society. Series B, Statistical methodology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Article
- Causal functional mediation analysis with an application to functional magnetic resonance imaging data.Biostatistics (Oxford, England) · 2025Article
- A New Integrated Interpolation Method for High Missing Unstable Disease Surveillance Data - 12 Urban Agglomerations, China, 2009-2020.China CDC weekly · 2024Article
- Scalar-Function Causal Discovery for Generating Causal Hypotheses with Observational Wearable Device Data.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2024Article
- Discovery and inference of a causal network with hidden confounding.Journal of the American Statistical Association · 2024Article
- Digital transformation in college libraries: The effect of digital reading on reader service satisfaction.PloS one · 2024Article
- Functional Bayesian networks for discovering causality from multivariate functional data.Biometrics · 2023Article
- Directed Cyclic Graph for Causal Discovery from Multivariate Functional Data.Advances in neural information processing systems · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
In this article, we introduce a functional structural equation model for estimating directional relations from multivariate functional data. We decouple the estimation into two major steps: directional order determination and selection through sparse functional regression. We first propose a score function at the linear operator level, and show that its minimization can recover the true directional order when the relation between each function and its parental functions is nonlinear. We then develop a sparse functional additive regression, where both the response and the multivariate predictors are functions and the regression relation is additive and nonlinear. We also propose strategies to speed up the computation and scale up our method. In theory, we establish the consistencies of order determination, sparse functional additive regression, and directed acyclic graph estimation, while allowing both the dimension of the Karhunen-Loéve expansion coefficients and the number of random functions to diverge with the sample size. We illustrate the efficacy of our method through simulations, and an application to brain effective connectivity analysis.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.