A Review of Causal Methods for High-Dimensional Data

A Review of Causal Methods for High-Dimensional Data

Causal learning from observational data is an important scientific endeavor, but the statistical and computational challenges posed by the high-dimensionality of many modern datasets are substantial. Peculiarities such as spurious correlations, endogeneity, noise accumulation, and deflated empirical...

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Bibliographic Details
Main Authors: Zewude A. Berkessa, Esa Laara, Patrik Waldmann
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Causal discovery
causal effect estimation
causal methods
confounding bias
endogeneity
high-dimensionality
Online Access:https://ieeexplore.ieee.org/document/10818663/
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