Causal inference meets deep learning: A comprehensive survey
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 …
approach may inadvertently capture spurious correlations within the data, leading to models …
Causal structure learning for high-dimensional non-stationary time series
S Chen, HT Wu, G Jin - Knowledge-Based Systems, 2024 - Elsevier
Learning the causal structure of high-dimensional non-stationary time series can help in
understanding the data generation mechanism, which is a crucial task in machine learning …
understanding the data generation mechanism, which is a crucial task in machine learning …
Sample Efficient Bayesian Learning of Causal Graphs from Interventions
Causal discovery is a fundamental problem with applications spanning various areas in
science and engineering. It is well understood that solely using observational data, one can …
science and engineering. It is well understood that solely using observational data, one can …
A Meta-Learning Approach to Bayesian Causal Discovery
Discovering a unique causal structure is difficult due to both inherent identifiability issues,
and the consequences of finite data. As such, uncertainty over causal structures, such as …
and the consequences of finite data. As such, uncertainty over causal structures, such as …
Continuous Bayesian Model Selection for Multivariate Causal Discovery
Current causal discovery approaches require restrictive model assumptions or assume
access to interventional data to ensure structure identifiability. These assumptions often do …
access to interventional data to ensure structure identifiability. These assumptions often do …
Causal Discovery Evaluation Framework in the Absence of Ground-Truth Causal Graph
T Li, L Wang, D Peng, J Liao, L Liu, Z Liu - IEEE Access, 2024 - ieeexplore.ieee.org
In causal learning, discovering the causal graph of the underlying generative mechanism
from observed data is crucial. However, real-world data for causal discovery is scarce and …
from observed data is crucial. However, real-world data for causal discovery is scarce and …
[HTML][HTML] Missing Data Imputation Based on Causal Inference to Enhance Advanced Persistent Threat Attack Prediction
X Cheng, M Kuang, H Yang - Symmetry, 2024 - mdpi.com
With the continuous development of network security situations, the types of attacks increase
sharply, but can be divided into symmetric attacks and asymmetric attacks. Symmetric …
sharply, but can be divided into symmetric attacks and asymmetric attacks. Symmetric …
Efficient Differentiable Discovery of Causal Order
In the algorithm Intersort, Chevalley et al.(2024) proposed a score-based method to discover
the causal order of variables in a Directed Acyclic Graph (DAG) model, leveraging …
the causal order of variables in a Directed Acyclic Graph (DAG) model, leveraging …
Variational Search Distributions
We develop variational search distributions (VSD), a method for finding discrete,
combinatorial designs of a rare desired class in a batch sequential manner with a fixed …
combinatorial designs of a rare desired class in a batch sequential manner with a fixed …
LOCAL: Learning with Orientation Matrix to Infer Causal Structure from Time Series Data
Discovering the underlying Directed Acyclic Graph (DAG) from time series observational
data is highly challenging due to the dynamic nature and complex nonlinear interactions …
data is highly challenging due to the dynamic nature and complex nonlinear interactions …