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In the automotive industry, the full cycle of managing in-use vehicle qualityissues can take weeks to investigate. The process involves isolating rootcauses, defining and implementing appropriate treatments, and refiningtreatments if needed. The main pain-point is the lack of a systematic method toidentify causal relationships, evaluate treatment effectiveness, and direct thenext actionable treatment if the current treatment was deemed ineffective. Thispaper will show how we leverage causal Machine Learning (ML) to speed up suchprocesses. A real-word data set collected from on-road vehicles will be used todemonstrate the proposed framework. Open challenges for vehicle qualityapplications will also be discussed.

A Novel Two-level Causal Inference Framework for On-road Vehicle Quality Issues Diagnosis