Directed cyclic graphs are a powerful tool for causal discovery in longitudinal observational data. They allow for the simultaneous discovery of time-lagged and instantaneous causality, which is crucial in understanding complex systems where variables may influence each other over time. The framework developed for this purpose is based on common causal discovery assumptions and incorporates instrumental variables, which are often available in longitudinal data. This approach is unique in its ability to achieve causal identifiability in directed graphs with cyclic patterns, a significant advancement in the field. The structural learning process is fully Bayesian, providing a robust statistical foundation for the model. Extensive simulations and real-world applications, such as the Women's Interagency HIV Study, have demonstrated the model's effectiveness and superiority over existing methods.
Bayesian inference, instrumental variables
Directed cyclic graph model
Women's Interagency HIV Study
Causal identifiability, model accuracy
Cloud-based, on-premises
Yes
Yes
Simultaneous discovery of time-lagged and instantaneous causality, Bayesian structural learning
Yes
Standard computational resources
Linux, Windows, macOS
Compatible with other statistical software
Data encryption, access control
GDPR, HIPAA
None
No
Limited community support
Academic researchers
Varies depending on the study
Low
Moderate
Graphical representation of causal relationships
Ensuring data privacy and ethical use of causal models
Complexity in handling large datasets
Healthcare, social sciences, economics
Causal analysis in epidemiological studies, policy impact assessment
Research institutions, universities
API integration with data analysis tools
Scalable with computational resources
Academic support, consulting services
None
Command-line interface, graphical user interface
Yes
English
Subscription-based, academic licensing
Yes
Collaborations with research institutions
None
Compliant with research ethics guidelines
1.0
Software
Yes
RESTful API for integration
Academic and research-focused
0.00
USD
Academic license
01/01/2023
01/10/2023
+1-800-555-0199
Integration with statistical software, support for complex causal models
Yes