A causal realization theory, part I; linear deterministic systems
SIAM Journal on Control and Optimization
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Discovering Temporal Rules from Temporally Ordered Data
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
TimeSleuth: A Tool for Discovering Causal and Temporal Rules
ICTAI '02 Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence
Automatic identification of quasi-experimental designs for discovering causal knowledge
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering temporal/causal rules: a comparison of methods
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
A rough set approach to mining connections from information systems
Proceedings of the 2010 ACM Symposium on Applied Computing
Using dependence diagrams to summarize decision rule sets
Canadian AI'08 Proceedings of the Canadian Society for computational studies of intelligence, 21st conference on Advances in artificial intelligence
A rough set approach to multiple dataset analysis
Applied Soft Computing
Temporal evolution and local patterns
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
The TIMERS II algorithm for the discovery of causality
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Learning bayesian networks in semi-deterministic systems
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
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In this paper we propose a solution to the problem of distinguishing between causal and acausal temporal sets of rules. The method, called the Temporal Investigation Method for Enregistered Record Sequences (TIMERS), is explained and introduced formally. The input to TIMERS consists of a sequence of records, where each record is observed at regular intervals. Sets of rules are generated from the input data using different window sizes and directions of time. The set of rules may describe an instantaneous relationship, where the decision attribute depends on condition attributes seen at the same time instant. We investigate the temporal characteristics of the system by changing the direction of time when generating temporal rules to see whether a set of rules is causal or acausal. The results are used to declare a verdict as to the nature of the system: instantaneous, causal, or acausal.