ArticleJournal of machine learning research : JMLR
Inference for a Large Directed Acyclic Graph with Unspecified Interventions.
Article in Journal of machine learning research : JMLR. 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.
- Bayesian causal graphical model for joint Mendelian randomization analysis of multiple exposures and outcomes.American journal of human genetics · 2025Article
- On network deconvolution for undirected graphs.Biometrics · 2024Article
- Article
- Causal Discovery with Generalized Linear Models through Peeling Algorithms.Journal of machine learning research : JMLR · 2024Article
- Discovery and inference of a causal network with hidden confounding.Journal of the American Statistical Association · 2024Article
- Nonlinear causal discovery with confounders.Journal of the American Statistical Association · 2024Article
- Inference of causal metabolite networks in the presence of invalid instrumental variables with GWAS summary data.Genetic epidemiology · 2023Article
- Inferring a directed acyclic graph of phenotypes from GWAS summary statistics.bioRxiv : the preprint server for biology · 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
3 authors.
Funding
Abstract
Statistical inference of directed relations given some unspecified interventions (i.e., the intervention targets are unknown) is challenging. In this article, we test hypothesized directed relations with unspecified interventions. First, we derive conditions to yield an identifiable model. Unlike classical inference, testing directed relations requires to identify the ancestors and relevant interventions of hypothesis-specific primary variables. To this end, we propose a peeling algorithm based on nodewise regressions to establish a topological order of primary variables. Moreover, we prove that the peeling algorithm yields a consistent estimator in low-order polynomial time. Second, we propose a likelihood ratio test integrated with a data perturbation scheme to account for the uncertainty of identifying ancestors and interventions. Also, we show that the distribution of a data perturbation test statistic converges to the target distribution. Numerical examples demonstrate the utility and effectiveness of the proposed methods, including an application to infer gene regulatory networks. The R implementation is available at https://github.com/chunlinli/intdag.
Indexed as
Identifiers
37701522PMC10497226What 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.