A theory of diagnosis from first principles
Artificial Intelligence
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
An Automated Fault Diagnosis System Using Hierarchical Reasoning and Alarm Correlation
Journal of Network and Systems Management
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Comparison of Rule-Based and Bayesian Network Approaches in Medical Diagnostic Systems
AIME '01 Proceedings of the 8th Conference on AI in Medicine in Europe: Artificial Intelligence Medicine
Temporal Causal Networks for Simulation and Diagnosis
ICECCS '96 Proceedings of the 2nd IEEE International Conference on Engineering of Complex Computer Systems
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Learning the Structure of Linear Latent Variable Models
The Journal of Machine Learning Research
Bioinformatics
Detection of Unfaithfulness and Robust Causal Inference
Minds and Machines
Dynamic Bayesian networks as prognostic models for clinical patient management
Journal of Biomedical Informatics
A dynamic Bayesian network for diagnosing ventilator-associated pneumonia in ICU patients
Expert Systems with Applications: An International Journal
CAA: a knowledge based system using causal knowledge to diagnose cardiac rhythm disorders
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
The temporal logic of causal structures
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Learning Non-Stationary Dynamic Bayesian Networks
The Journal of Machine Learning Research
An algorithmic enquiry concerning causality
An algorithmic enquiry concerning causality
Inference in hybrid networks: theoretical limits and practical algorithms
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Causal inference in the presence of latent variables and selection bias
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
A model-oriented framework for runtime monitoring of nonfunctional properties
QoSA'05 Proceedings of the First international conference on Quality of Software Architectures and Software Quality, and Proceedings of the Second International conference on Software Quality
A logic for causal inference in time series with discrete and continuous variables
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Facilitating the analysis of discourse phenomena in an interoperable NLP platform
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
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Causality is an important concept throughout the health sciences and is particularly vital for informatics work such as finding adverse drug events or risk factors for disease using electronic health records. While philosophers and scientists working for centuries on formalizing what makes something a cause have not reached a consensus, new methods for inference show that we can make progress in this area in many practical cases. This article reviews core concepts in understanding and identifying causality and then reviews current computational methods for inference and explanation, focusing on inference from large-scale observational data. While the problem is not fully solved, we show that graphical models and Granger causality provide useful frameworks for inference and that a more recent approach based on temporal logic addresses some of the limitations of these methods.