Workshop: Causal Machine Learning for Real-World Impact

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

Qian Wang · Huanyi Shui · Thi Tu Trinh Tran · Milad nezhad · devesh upadhyay · Kamran Paynabar · Anqi He


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.

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