Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Model checking
Outlier detection for high dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Mining needle in a haystack: classifying rare classes via two-phase rule induction
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Information-Theoretic Measures for Anomaly Detection
SP '01 Proceedings of the 2001 IEEE Symposium on Security and Privacy
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
Causality and Counterfactuals in the Situation Calculus
Journal of Logic and Computation
Bioinformatics
ACM Computing Surveys (CSUR)
The temporal logic of causal structures
UAI '09 Proceedings of the Twenty-Fifth 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
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Large-scale observational datasets are prevalent in many areas of research, including biomedical informatics, computational social science, and finance. However, our ability to use these data for decision-making lags behind our ability to collect and mine them. One reason for this is the lack of methods for inferring the causal impact of rare events. In cases such as the monitoring of continuous data streams from intensive care patients, social media, or finance, though, rare events may in fact be the most important ones-signaling critical changes in a patient's status or trading volume. While prior data mining approaches can identify or predict rare events, they cannot determine their impact, and probabilistic causal inference methods fail to handle inference with infrequent events. Instead, we develop a new approach to finding the causal impact of rare events that leverages the large amount of data available to infer a model of a system's functioning and evaluates how rare events explain deviations from usual behavior. Using simulated data, we evaluate the approach and compare it against others, demonstrating that it can accurately infer the effects of rare events.