Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Model checking
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Symbolic Model Checking for Probabilistic Processes
ICALP '97 Proceedings of the 24th International Colloquium on Automata, Languages and Programming
Analyzing time series gene expression data
Bioinformatics
Learning the structure of dynamic probabilistic networks
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Artificial Intelligence in Medicine
Methodological Review: A review of causal inference for biomedical informatics
Journal of Biomedical Informatics
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
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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Computational analysis of time-course data with an underlying causal structure is needed in a variety of domains, including neural spike trains, stock price movements, and gene expression levels. However, it can be challenging to determine from just the numerical time course data alone what is coordinating the visible processes, to separate the underlying prima facie causes into genuine and spurious causes and to do so with a feasible computational complexity. For this purpose, we have been developing a novel algorithm based on a framework that combines notions of causality in philosophy with algorithmic approaches built on model checking and statistical techniques for multiple hypotheses testing. The causal relationships are described in terms of temporal logic formulæ, reframing the inference problem in terms of model checking. The logic used, PCTL, allows description of both the time between cause and effect and the probability of this relationship being observed. We show that equipped with these causal formulæ with their associated probabilities we may compute the average impact a cause makes to its effect and then discover statistically significant causes through the concepts of multiple hypothesis testing (treating each causal relationship as a hypothesis), and false discovery control. By exploring a well-chosen family of potentially all significant hypotheses with reasonably minimal description length, it is possible to tame the algorithm's computational complexity while exploring the nearly complete search-space of all prima facie causes. We have tested these ideas in a number of domains and illustrate them here with two examples.