ArticleFoundations of computational mathematics (New York, N.Y.)2022
Causal Structure Learning: A Combinatorial Perspective.
Article in Foundations of computational mathematics (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Celcomen: spatial causal disentanglement for single-cell and tissue perturbation modeling.Nature communications · 2026Article
- Causal discovery methods in psychological research: Foundations, algorithms, and a practical tutorial in R.Behavior research methods · 2026Review
- dotears: Scalable and consistent directed acyclic graph estimation using observational and interventional data.iScience · 2025Article
- Molecular causality in the advent of foundation models.Molecular systems biology · 2024Review
- Kernel-Based Independence Tests for Causal Structure Learning on Functional Data.Entropy (Basel, Switzerland) · 2023Article
- Causal identification of single-cell experimental perturbation effects with CINEMA-OT.Nature methods · 2023Article
- Discovery of interconnected causal drivers of COVID-19 vaccination intentions in the US using a causal Bayesian network.Scientific reports · 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 review, we discuss approaches for learning causal structure from data, also called
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.