MPhil/PhD Statistics (Assessing inequality in the Criminal Justice System using novel causal inference methods and Bayesian spatial models)

该项目的目的是使用因果推理方法，通过使用数据优先：刑事司法系统关联数据集量化判刑中歧视的影响，来证实拉米评论（2017）的结论。理解的总体目标是:是否以及如何早期生活课程教育,保健等事件影响系统discriminationHow早期经历影响参与刑事司法系统在以后的生活中,是否歧视继续发挥作用是否存在地区差异(例如伦敦和英国其他)使用下列方法:纵向中介分析来确定在保健和教育的影响对暴力和逮捕等后来结果的了解，以及歧视如何混淆这些关系；近缘因果推理，以确定歧视对与暴力和刑事司法系统参与有关的生活事件之间途径的影响；贝叶斯空间分析解决空间/区域歧视差异。

The aim of this project is to use causal inference methods to corroborate the conclusions of the Lammy Review (2017) by quantifying the impact of discrimination in sentencing using Data First: Criminal Justice System Linked Datasets.The overall aims are to understand:Whether and how early life course events such as education and being in care are impacted by systematic discriminationHow early experiences impact involvement with the Criminal Justice system later in life and whether discrimination continues to play a part Whether there are regional differences (e.g. London vs the rest of England)The following methods are to be used:Longitudinal mediation analysis to determine the impact of being in care and educational attainment on later outcomes such as violence and arrest and how discrimination confounds these relationships;Proximal causal inference to determine the impact of discrimination on the pathway between life events in relation to violence and involvement with the Criminal Justice System;Bayesian Spatial analysis to address spatial/regional differences in discrimination.