Our goal is to automatically discover “causal relationships” from time series data, i.e., a sequence of data
measured over time. Discovering causal relationships has key applications in various fields: e.g., finding that
“R&D expenditure influences sales” is useful for decision making in companies; discovering gene regulatory
relationships provides a key insight for drug discovery researches.
To infer causal relationships, existing methods require us to select an appropriate mathematical expression (i.e.,
auto-regressive model) for each time series data, which is difficult without expertise in data analysis. For this
problem, we build a novel approach that trains a machine learning model by using various data. Our method does
not require a deep understanding of data analysis and therefore will help us to effectively make an important
decision making in several situations.
Yoichi Chikahara, Learning and Intelligent Systems Research Group, Innovative Communication Laboratory