A Novel Two-Level Causal Inference Framework for On-Road Vehicle Quality Issues Diagnosis
Abstract
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.
Cite
Text
Wang et al. "A Novel Two-Level Causal Inference Framework for On-Road Vehicle Quality Issues Diagnosis." NeurIPS 2022 Workshops: CML4Impact, 2022.Markdown
[Wang et al. "A Novel Two-Level Causal Inference Framework for On-Road Vehicle Quality Issues Diagnosis." NeurIPS 2022 Workshops: CML4Impact, 2022.](https://mlanthology.org/neuripsw/2022/wang2022neuripsw-novel/)BibTeX
@inproceedings{wang2022neuripsw-novel,
title = {{A Novel Two-Level Causal Inference Framework for On-Road Vehicle Quality Issues Diagnosis}},
author = {Wang, Qian and Shui, Huanyi and Tran, Thi Tu Trinh and Nezhad, Milad Zafar and Upadhyay, Devesh and Paynabar, Kamran and He, Anqi},
booktitle = {NeurIPS 2022 Workshops: CML4Impact},
year = {2022},
url = {https://mlanthology.org/neuripsw/2022/wang2022neuripsw-novel/}
}