Abstract
Racial disproportionality in Stop & 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 Bayesian empirical likelihood procedure combined with double machine learning techniques to control for high-dimensional confounding and to accommodate the strong prior assumption. In addition, we show that the proposed procedure generates a valid posterior in terms of coverage. Applying this approach to the Stop & 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.
| Original language | English |
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| Publication status | Published - 11 Feb 2025 |
Keywords
- stat.AP
- stat.ME
