Version 1
: Received: 6 March 2024 / Approved: 7 March 2024 / Online: 7 March 2024 (11:07:17 CET)
How to cite:
Thorjussen, C.B.H.; Liland, K.H.; Solberg, L.E.; Måge, I. A Computational Procedure for Testing Conditional Independence in Causal Directed Acyclic Graphs. Preprints2024, 2024030423. https://doi.org/10.20944/preprints202403.0423.v1
Thorjussen, C.B.H.; Liland, K.H.; Solberg, L.E.; Måge, I. A Computational Procedure for Testing Conditional Independence in Causal Directed Acyclic Graphs. Preprints 2024, 2024030423. https://doi.org/10.20944/preprints202403.0423.v1
Thorjussen, C.B.H.; Liland, K.H.; Solberg, L.E.; Måge, I. A Computational Procedure for Testing Conditional Independence in Causal Directed Acyclic Graphs. Preprints2024, 2024030423. https://doi.org/10.20944/preprints202403.0423.v1
APA Style
Thorjussen, C.B.H., Liland, K.H., Solberg, L.E., & Måge, I. (2024). A Computational Procedure for Testing Conditional Independence in Causal Directed Acyclic Graphs. Preprints. https://doi.org/10.20944/preprints202403.0423.v1
Chicago/Turabian Style
Thorjussen, C.B.H., Lars Erik Solberg and Ingrid Måge. 2024 "A Computational Procedure for Testing Conditional Independence in Causal Directed Acyclic Graphs" Preprints. https://doi.org/10.20944/preprints202403.0423.v1
Abstract
This study introduces a novel computational approach for testing conditional independence (BB CI test) within causal Directed Acyclic Graphs (DAGs), leveraging Bayesian non-parametric bootstrap and machine learning techniques. Our method offers an alternative for validating the assumptions underpinning causal DAGs. Through simulation studies and an industrial case analysis, we demonstrate the test procedure in accurately assessing conditional independence, comparing it with the Generalized Covariance Measure (GCM) test. Our findings suggest that the BB CI test is advantageous in scenarios where existing methods may falter due to violations of model assumptions. This research contributes to the causal inference literature by providing a computational tool for researchers and practitioners to validate causal models.
Computer Science and Mathematics, Probability and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
