Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
A Model Checking Approach to the Parameter Estimation of Biochemical Pathways
CMSB '08 Proceedings of the 6th International Conference on Computational Methods in Systems Biology
The temporal logic of causal structures
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
A logic and time nets for probabilistic inference
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
Annotated probabilistic temporal logic
ACM Transactions on Computational Logic (TOCL)
An algorithmic enquiry concerning causality
An algorithmic enquiry concerning causality
A variational approximation for Bayesian networks with discrete and continuous latent variables
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Inference in hybrid networks: theoretical limits and practical algorithms
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Methodological Review: A review of causal inference for biomedical informatics
Journal of Biomedical Informatics
Causal inference with rare events in large-scale time-series data
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Many applications of causal inference, such as finding the relationship between stock prices and news reports, involve both discrete and continuous variables observed over time. Inference with these complex sets of temporal data, though, has remained difficult and required a number of simplifications. We show that recent approaches for inferring temporal relationships (represented as logical formulas) can be adapted for inference with continuous valued effects. Building on advances in logic, PCTLc (an extension of PCTL with numerical constraints) is introduced here to allow representation and inference of relationships with a mixture of discrete and continuous components. Then, finding significant relationships in the continuous case can be done using the conditional expectation of an effect, rather than its conditional probability. We evaluate this approach on both synthetically generated and actual financial market data, demonstrating that it can allow us to answer different questions than the discrete approach can.