ArticleJournal of machine learning research : JMLR2019
The Reduced PC-Algorithm: Improved Causal Structure Learning in Large Random Networks.
Article in Journal of machine learning research : JMLR, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Distribution-valued data graphical model estimation based on M-LDQ feature embedding.Journal of applied statistics · 2026Article
- Post-selection inference for causal effects after causal discovery.Biometrika · 2025Article
- Causal Structural Learning via Local Graphs.SIAM journal on mathematics of data science · 2023Article
- Consistent Second-Order Conic Integer Programming for Learning Bayesian Networks.Journal of machine learning research : JMLR · 2023Article
- Nonparametric Causal Structure Learning in High Dimensions.Entropy (Basel, Switzerland) · 2022Article
- Integer Programming for Learning Directed Acyclic Graphs from Continuous Data.INFORMS journal on optimization · 2021Article
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
We consider the task of estimating a high-dimensional directed acyclic graph, given observations from a linear structural equation model with arbitrary noise distribution. By exploiting properties of common random graphs, we develop a new algorithm that requires conditioning only on small sets of variables. The proposed algorithm, which is essentially a modified version of the PC-Algorithm, offers significant gains in both computational complexity and estimation accuracy. In particular, it results in more efficient and accurate estimation in large networks containing hub nodes, which are common in biological systems. We prove the consistency of the proposed algorithm, and show that it also requires a less stringent faithfulness assumption than the PC-Algorithm. Simulations in low and high-dimensional settings are used to illustrate these findings. An application to gene expression data suggests that the proposed algorithm can identify a greater number of clinically relevant genes than current methods.
Identifiers
37799538PMC10552884What 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.