Abstract
Causal Structure Discovery (CSD) is the task of learning the set of underlying causal relationships from observational data. Due to their computational scalability and flexibility, a recently developed class of CSD methods, NOTEARS, based on a formulation allowing for continuous optimization, is gaining popularity. However, this formulation can lead to incorrect edge orientations when little/no likelihood advantage is conferred upon any edge orientation, e.g., a → b → c versus c → b → a. In longitudinal data, like electronic health records (EHRs), temporal relationships are observable among many pairs of variables. Such temporal relationship is imperfect but still suggest an orientation since causal effects cannot travel backwards in time. Following this idea, we propose methods to incorporate precedence constraints into continuous optimization-based CSD methods. Experiments on both a synthetic and two real-world datasets validate the effectiveness of the proposed precedence constraints.
Original language | English (US) |
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Title of host publication | Proceedings - 2023 IEEE 11th International Conference on Healthcare Informatics, ICHI 2023 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1-11 |
Number of pages | 11 |
ISBN (Electronic) | 9798350302639 |
DOIs | |
State | Published - 2023 |
Event | 11th IEEE International Conference on Healthcare Informatics, ICHI 2023 - Houston, United States Duration: Jun 26 2023 → Jun 29 2023 |
Publication series
Name | Proceedings - 2023 IEEE 11th International Conference on Healthcare Informatics, ICHI 2023 |
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Conference
Conference | 11th IEEE International Conference on Healthcare Informatics, ICHI 2023 |
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Country/Territory | United States |
City | Houston |
Period | 6/26/23 → 6/29/23 |
Bibliographical note
Publisher Copyright:© 2023 IEEE.
Keywords
- Causality
- Healthcare
- Machine Learning