Neural Causality Detection for Multi-dimensional Point Processes

Abstract

In the big data era, while correlation detection is relatively straightforward and successfully addressed by many techniques, causality detection does not have a generally-used solution. Causality provides valuable insights into data and guides further studies. With the overall assumption that causal influence can only be from prior history events, time plays an essential part in causality analysis, and this important feature means the data with strict temporal structure needs to be modelled. Traditionally, temporal point processes are employed to model data containing temporal structure information. The heuristic parameterization property of such models makes the task difficult. Domain related knowledge are needed to design proper parameterization. Recently, Recurrent Neural Networks (RNNs) have been used for time-related data modelling. RNN’s trainable parameterization considerably reduces the dependency on domain-related knowledge. In this work, we show that combining neural network techniques with Granger causality framework has great potential by presenting an RNN model integrated with a Granger causality framework. The experimental results show that the same network structure can be applied to a variety of datasets and causalities are detected successfully.