Racial disproportionality in Stop and Search practices elicits substantial concerns about its societal and behavioral impacts. This paper aims to investigate the effect of disproportionality, particularly on the black community, on expressive crimes in London using data from January 2019 to December 2023. We focus on a semi-parametric partially linear structural regression method and introduce a scalable Bayesian empirical likelihood procedure combined with double machine learning techniques to control for high-dimensional confounding and to accommodate strong prior assumptions. In addition, we show that the proposed procedure yields a valid posterior in terms of coverage. Applying this approach to the Stop and Search dataset, we find that racial disproportionality aimed at the Black community may be alleviated by taking into account the proportion of the Black population when focusing on expressive crimes.

翻译：执法中“拦截与搜查”实践中的种族比例失衡现象引发了对其社会与行为影响的重大关切。本文旨在利用2019年1月至2023年12月的数据，研究伦敦地区种族比例失衡（特别是针对黑人群体）对情绪型犯罪的影响。我们聚焦于半参数部分线性结构回归方法，引入了一种可扩展的贝叶斯经验似然程序，该程序结合双重机器学习技术以控制高维混杂因素并适应强先验假设。此外，我们证明所提程序在覆盖概率意义上产生有效的后验分布。将该方法应用于“拦截与搜查”数据集，我们发现当聚焦于情绪型犯罪时，通过考虑黑人人口比例可能缓解针对黑人社区的种族比例失衡问题。