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职业安全政策对企业违约的因果影响评估,使用机器学习提升建模
Berardino Barile1,2, Marco Forti3, Alessia Marrocco3
1Centre for Intelligent Machines, Department of Electrical and Computer Engineering, McGill University, Montréal, Canada. berardino.barile@mcgill.ca.
Scientific reports
|May 6, 2024
概括
职业安全政策间接地促进了企业的生存. 机器学习模型,特别是LightGBM,有效地评估了意大利的援助计划,确定了从安全投资中获益最多的公司.
科学领域:
- 经济学 经济学 经济学
- 职业健康和安全问题 职业健康和安全问题
- 数据科学数据科学数据科学
背景情况:
- 职业安全政策对工人的福利和企业的经济表现都有影响.
- 传统上,计量经济学模型主导着因果关系分析,机器学习 (ML) 模型最初面临怀疑.
- 越来越复杂的数据集需要非线性关系的自动化算法,突出了ML在因果推断中的潜力.
研究的目的:
- 评估一项意大利的公共援助计划,支持中小企业 (中小企业) 在职业安全和健康 (OSH) 方面的投资.
- 评估这些OHS投资对企业生存率的影响.
- 为了比较13种不同的模型在估计个人治疗效应 (ITE) 的有效性.
主要方法:
- 利用机器学习模型来处理因果推理中的复杂,非线性关系.
- 对13种不同的模型进行了比较分析,以估计个人治疗效应 (ITE).
- 使用AUUC和Qini分数验证模型性能,光梯度增强机 (LightGBM) 显示出优异的结果.
主要成果:
- 轻度梯度增强机 (LightGBM) 模型实现了最佳性能,AUUC为0.064和Qini得分为0.407.
- 该研究发现,在干预前遇到绩效问题的企业是该援助计划的主要受益者.
- 政策干预增加的流动性对于防止这些处于风险的公司的违约至关重要.
结论:
- 机器学习模型为经济和政策评估中的因果推理提供了强大的工具.
- 意大利的职业安全和健康援助计划有效地支持企业的生存,特别是对脆弱的中小企业.
- 与安全投资相关的有针对性的财政支持可以成为防止企业失败的关键干预措施.
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