ReviewResearch (Washington, D.C.)2024
Causal Inference Meets Deep Learning: A Comprehensive Survey.
Review in Research (Washington, D.C.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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
27 citing papers in PubMed.
- A causal reinforcement learning framework for reliable gene regulatory network inference.BMC bioinformatics · 2026Article
- DeepCas12a: a hybrid deep learning framework for accurate AsCas12a efficiency prediction from sequence and epigenetic information.BMC genomics · 2026Article
- A causal discovery framework for analyzing the impact mechanism of generative AI on digital literacy: an LLM-based behavioral analysis approach.Scientific reports · 2026Article
- Causal Structure Learning Assumptions Shape Counterfactual Safety: Expert-Guided Constraints vs. Data-Driven DAGs with Probabilistic Logic Twin Networks.Entropy (Basel, Switzerland) · 2026Article
- Visible-Near-Infrared Hyperspectral Imaging Enables Nondestructive Identification of Bean Accessions via 1D Spectral Reflectance Analysis.ACS omega · 2026Article
- Multi-layer causal knowledge network for product quality problem analysis using third data.Scientific reports · 2026Article
- CSFPR-RTDETR-CR: A Causal Intervention Enhanced Framework for Infrared UAV Small Target Detection with Feature Debiasing.Sensors (Basel, Switzerland) · 2026Article
- Causal Discovery in Observational Medical Research: Scoping Review.JMIR medical informatics · 2026Article
- Development and validation of explainable machine learning models for the prediction of survival in patients with M1 breast cancer.Gland surgery · 2026Article
- A causal discovery-based adaptive fusion algorithm for multi-source heterogeneous knowledge graphs.Scientific reports · 2026Article
- Mechanism-informed machine learning for interpretable drug release prediction and virtual formulation intervention in polymeric long-acting injectables.Frontiers in medicine · 2026Article
- Foundation Models Meet Medical Image Interpretation.Research (Washington, D.C.) · 2026Review
- A Diffusion-Based Digital Twin for Predicting Road Deterioration under Climate and Traffic Stress.Research (Washington, D.C.) · 2026Article
- Application of multimodal data fusion and intelligent classification in medical coding with the MCoder-T model.PloS one · 2026Article
- A neuromuscular clinician's primer on machine learning.Journal of neuromuscular diseases · 2026Review
- Artificial intelligence in neurocardiology: decoding brain-heart network interactions for clinical and translational insights.Frontiers in neuroscience · 2026Review
- Article
- CAUSALRLSTACK: adaptive balancing of deep representation and causal effect estimation with application to HIV-related health data.BioData mining · 2025Article
- Non-linear association between visceral adipose tissue area and serum uric acid concentration in US adults: findings from NHANES 2011-2018.BMC public health · 2025Article
- Identification of Critical States in Complex Biological Systems Using Cell-Specific Causal Network Entropy.Research (Washington, D.C.) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning relies on learning from extensive data to generate prediction results. This approach may inadvertently capture spurious correlations within the data, leading to models that lack interpretability and robustness. Researchers have developed more profound and stable causal inference methods based on cognitive neuroscience. By replacing the correlation model with a stable and interpretable causal model, it is possible to mitigate the misleading nature of spurious correlations and overcome the limitations of model calculations. In this survey, we provide a comprehensive and structured review of causal inference methods in deep learning. Brain-like inference ideas are discussed from a brain-inspired perspective, and the basic concepts of causal learning are introduced. The article describes the integration of causal inference with traditional deep learning algorithms and illustrates its application to large model tasks as well as specific modalities in deep learning. The current limitations of causal inference and future research directions are discussed. Moreover, the commonly used benchmark datasets and the corresponding download links are summarized.
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.