ArticleJournal of the American Statistical Association2024
Nonlinear causal discovery with confounders.
Article in Journal of the American Statistical Association, 2024. 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
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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.
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.
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Who cites it
6 citing papers in PubMed.
- NLCD: A method to discover nonlinear causal relations among genes.PLoS computational biology · 2026Article
- Tree-based additive noise directed acyclic graphical models for nonlinear causal discovery with interactions.Biometrics · 2025Article
- Discovery and inference of a causal network with hidden confounding.Journal of the American Statistical Association · 2024Article
- Causal Inference Meets Deep Learning: A Comprehensive Survey.Research (Washington, D.C.) · 2024Review
- Association between COVID-19 and sensorineural hearing loss: Evidence from a Mendelian randomization study in European and East Asian population.Immunity, inflammation and disease · 2023Article
- Learning Causal Biological Networks with Parallel Ant Colony Optimization Algorithm.Bioengineering (Basel, Switzerland) · 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
This article introduces a causal discovery method to learn nonlinear relationships in a directed acyclic graph with correlated Gaussian errors due to confounding. First, we derive model identifiability under the sublinear growth assumption. Then, we propose a novel method, named the Deconfounded Functional Structure Estimation (DeFuSE), consisting of a deconfounding adjustment to remove the confounding effects and a sequential procedure to estimate the causal order of variables. We implement DeFuSE via feedforward neural networks for scalable computation. Moreover, we establish the consistency of DeFuSE under an assumption called the strong causal minimality. In simulations, DeFuSE compares favorably against state-of-the-art competitors that ignore confounding or nonlinearity. Finally, we demonstrate the utility and effectiveness of the proposed approach with an application to gene regulatory network analysis. The Python implementation is available at https://github.com/chunlinli/defuse.
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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.