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Learning Linear Gaussian Polytree Models with Interventions
- Publication Year :
- 2023
-
Abstract
- We present a consistent and highly scalable local approach to learn the causal structure of a linear Gaussian polytree using data from interventional experiments with known intervention targets. Our methods first learn the skeleton of the polytree and then orient its edges. The output is a CPDAG representing the interventional equivalence class of the polytree of the true underlying distribution. The skeleton and orientation recovery procedures we use rely on second order statistics and low-dimensional marginal distributions. We assess the performance of our methods under different scenarios in synthetic data sets and apply our algorithm to learn a polytree in a gene expression interventional data set. Our simulation studies demonstrate that our approach is fast, has good accuracy in terms of structural Hamming distance, and handles problems with thousands of nodes.<br />Comment: To be published in: IEEE Journal on Selected Areas in Information Theory, Special Issue: Causality: Fundamental Limits and Applications
- Subjects :
- Statistics - Machine Learning
Computer Science - Machine Learning
Subjects
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2311.04636
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1109/JSAIT.2023.3328429