Discovering spatio-temporal causal interactions in traffic data streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable and automated workflow in mining large-scale severe-storm simulations
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
Overlapping decomposition for causal graphical modeling
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the Sixth International Workshop on Knowledge Discovery from Sensor Data
Information-theoretic measures of influence based on content dynamics
Proceedings of the sixth ACM international conference on Web search and data mining
GCBN: a hybrid spatio-temporal causal model for traffic analysis and prediction
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
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Attribution of climate change to causal factors has been based predominantly on simulations using physical climate models, which have inherent limitations in describing such a complex and chaotic system. We propose an alternative, data centric, approach that relies on actual measurements of climate observations and human and natural forcing factors. Specifically, we develop a novel method to infer causality from spatial-temporal data, as well as a procedure to incorporate extreme value modeling into our method in order to address the attribution of extreme climate events, such as heatwaves. Our experimental results on a real world dataset indicate that changes in temperature are not solely accounted for by solar radiance, but attributed more significantly to CO2 and other greenhouse gases. Combined with extreme value modeling, we also show that there has been a significant increase in the intensity of extreme temperatures, and that such changes in extreme temperature are also attributable to greenhouse gases. These preliminary results suggest that our approach can offer a useful alternative to the simulation-based approach to climate modeling and attribution, and provide valuable insights from a fresh perspective.