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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10818663/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832590330798538752 |
|---|---|
| author | Zewude A. Berkessa Esa Laara Patrik Waldmann |
| author_facet | Zewude A. Berkessa Esa Laara Patrik Waldmann |
| author_sort | Zewude A. Berkessa |
| collection | DOAJ |
| description | 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 covariance estimation complicate analysis. These issues may lead to confounding bias, which can be misleading when attempting to learn the true causal relationships and causal effects between variables. In this survey, we provide a comprehensive review of causal analysis and the theory behind high-dimensionality. We discuss the effects of high-dimensionality on causal estimation methods and their corresponding solutions. Finally, we present evaluation metrics and software tools for both causal effect estimation and causal discovery. |
| format | Article |
| id | doaj-art-65dd747491bb4390b68fe5bbf5f4130f |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-65dd747491bb4390b68fe5bbf5f4130f2025-01-24T00:01:32ZengIEEEIEEE Access2169-35362025-01-0113118921191710.1109/ACCESS.2024.352426110818663A Review of Causal Methods for High-Dimensional DataZewude A. Berkessa0https://orcid.org/0009-0005-8899-7494Esa Laara1https://orcid.org/0000-0001-6296-2377Patrik Waldmann2https://orcid.org/0000-0003-2390-6609Research Unit of Mathematical Sciences, University of Oulu, Oulu, FinlandResearch Unit of Mathematical Sciences, University of Oulu, Oulu, FinlandResearch Unit of Mathematical Sciences, University of Oulu, Oulu, FinlandCausal 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 covariance estimation complicate analysis. These issues may lead to confounding bias, which can be misleading when attempting to learn the true causal relationships and causal effects between variables. In this survey, we provide a comprehensive review of causal analysis and the theory behind high-dimensionality. We discuss the effects of high-dimensionality on causal estimation methods and their corresponding solutions. Finally, we present evaluation metrics and software tools for both causal effect estimation and causal discovery.https://ieeexplore.ieee.org/document/10818663/Causal discoverycausal effect estimationcausal methodsconfounding biasendogeneityhigh-dimensionality |
| spellingShingle | Zewude A. Berkessa Esa Laara Patrik Waldmann A Review of Causal Methods for High-Dimensional Data IEEE Access Causal discovery causal effect estimation causal methods confounding bias endogeneity high-dimensionality |
| title | A Review of Causal Methods for High-Dimensional Data |
| title_full | A Review of Causal Methods for High-Dimensional Data |
| title_fullStr | A Review of Causal Methods for High-Dimensional Data |
| title_full_unstemmed | A Review of Causal Methods for High-Dimensional Data |
| title_short | A Review of Causal Methods for High-Dimensional Data |
| title_sort | review of causal methods for high dimensional data |
| topic | Causal discovery causal effect estimation causal methods confounding bias endogeneity high-dimensionality |
| url | https://ieeexplore.ieee.org/document/10818663/ |
| work_keys_str_mv | AT zewudeaberkessa areviewofcausalmethodsforhighdimensionaldata AT esalaara areviewofcausalmethodsforhighdimensionaldata AT patrikwaldmann areviewofcausalmethodsforhighdimensionaldata AT zewudeaberkessa reviewofcausalmethodsforhighdimensionaldata AT esalaara reviewofcausalmethodsforhighdimensionaldata AT patrikwaldmann reviewofcausalmethodsforhighdimensionaldata |