Generating Causal Hypotheses for Explaining Black-Box Industrial Processes

Industrial processes nowadays are complex net-works of various modules. Due to their complexity, the processes are black-boxes, i.e. dependencies between input and output products of the process are mainly unknown. Getting insights into the process is challenging, since the extent of the data collected is usually to large to allow for a direct interpretation. Causal dependencies, i.e. the effect of an input variable on an output variable, allow for explaining what is happening inside the process. However, it is well known, that the identification of causal dependencies cannot be done purely data-driven but always requires at least a minimum of expert knowledge. In this article, a data-driven approach for the generation of causal hypotheses, i.e. dependencies in the data that might indicate a causal dependency, is presented. The approach is based on correlating input and output data of the black-box approach and quantifying the found correlation using regression. The approach is applied to an industrial compounding process and it is shown, that significant causal hypotheses can be found. These hypotheses are later validated by process experts.