# Causal factors discovering from Chinese construction accident cases

arXiv: https://arxiv.org/abs/2105.01227 · HTML: https://mlsanity.com/paper/2105.01227/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 52 references that are in the map.
- First posted on arXiv 4 May 2021.

## Builds on (most cited first, 1 of 1)

- [Review of The Book of Why: The New Science of Cause and Effect](https://mlsanity.com/paper/2003.11635/) (2020)

## Abstract (arXiv)

In China, construction accidents have killed more people than any other industry since 2012. The factors which led to the accident have complex interaction. Real data about accidents is the key to reveal the mechanism among these factors. But the data from the questionnaire and interview has inherent defects. Many behaviors that impact safety are illegal. In China, most of the cases are from accident investigation reports. Finding out the cause of the accident and liability affirmation are the core of incident investigation reports. So the truth of some answers from the respondents is doubtful. With a series of NLP technologies, in this paper, causal factors of construction accidents are extracted and organized from Chinese incident case texts. Finally, three kinds of neglected causal factors are discovered after data analysis.