A scalable Bayesian double machine learning framework for high dimensional causal estimation, with application to racial disproportionality assessment

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.

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