Prospects of Continual Causality for Industrial Applications

Daigo Fujiwara, Kazuki Koyama, Keisuke Kiritoshi, Tomomi Okawachi, Tomonori Izumitani, Shohei Shimizu

Abstract: We have been investigating the causal analysis of industrial plant process data and its various applications, such as material quantity optimization utilizing intervention effects. However, process data often comes with various problems such as non-stationary characteristics including distribution shifts, which make such applications difficult. When combined with the idea of continual learning, causal models may be able to solve these problems. We present the potential and prospects for industrial applications of continual causality, showing previous work. We also briefly introduce our causal discovery method utilizing a continual framework.