  In the automotive industry, the full cycle of managing in-use vehicle quality
issues can take weeks to investigate. The process involves isolating root
causes, defining and implementing appropriate treatments, and refining
treatments if needed. The main pain-point is the lack of a systematic method to
identify causal relationships, evaluate treatment effectiveness, and direct the
next actionable treatment if the current treatment was deemed ineffective. This
paper will show how we leverage causal Machine Learning (ML) to speed up such
processes. A real-word data set collected from on-road vehicles will be used to
demonstrate the proposed framework. Open challenges for vehicle quality
applications will also be discussed.
