ITGH: Information-Theoretic Granger Causal Inference on Heterogeneous Data

Author(s)
Sahar Behzadi, Benjamin Schelling, Claudia Plant
Abstract

Granger causality for time series states that a cause improves the predictability of its effect. That is, given two time series x and y, we are interested in detecting the causal relations among them considering the previous observations of both time series. Although, most of the algorithms are designed for causal inference among homogeneous processes where only time series from a specific distribution (mostly Gaussian) are given, many applications generate a mixture of various time series from different distributions. We utilize Generalized Linear Models (GLM) to propose a general information-theoretic framework for causal inference on heterogeneous data sets. We regard the challenge of causality detection as a data compression problem employing the Minimum Description Length (MDL) principle. By balancing the goodness-of-fit and the model complexity we automatically find the causal relations. Extensive experiments on synthetic and real-world data sets confirm the advantages of our algorithm ITGH (for Information-Theoretic Granger causal inference on Heterogeneous data) compared to other algorithms.

Organisation(s)
Research Group Data Mining and Machine Learning, Research Network Data Science
Pages
742-755
No. of pages
14
Publication date
2020
Peer reviewed
Yes
Austrian Fields of Science 2012
102033 Data mining
Portal url
https://ucris.univie.ac.at/portal/en/publications/itgh-informationtheoretic-granger-causal-inference-on-heterogeneous-data(66581957-6dc3-4181-b0d1-066dc434fc88).html