This study examines the relationship between houselessness and recidivism among people on probation with and without behavioral health problems. The study also illustrates a new way to summarize the effect of an exposure on an outcome, the Incremental Propensity Score (IPS), which avoids pitfalls of other estimation approaches commonly used in criminology. We assessed the impact of houselessness at probation start on rearrest within one year among a cohort of people on probation (n = 2,453). We estimated IPS effects, considering general and crime-specific recidivism if subjects were more or less likely to be unhoused and assessed effect variation by psychiatric disorder status. We used a doubly robust machine learning estimator to flexibly but efficiently estimate effects. Decreasing houselessness led to a lower estimated average rate of recidivism. Dividing the odds of houselessness by ten had a significant effect when compared to multiplying the odds of houselessness by ten, corresponding to a 9% reduction in the estimated average rate of recidivism (p < 0.05). Milder interventions showed smaller, non-significant effect sizes. Stratifying by diagnoses and re-arrest type led to similar results without statistical significance. Minding limitations related to observational data and generalizability, this study supports houselessness as a risk factor for recidivism across populations with a new analytic approach. Efforts to reduce recidivism should include interventions that make houselessness less likely, such as increasing housing access. Meanwhile, efforts to establish recidivism risk factors should consider alternative effects like IPS effects to maximize validity and reduce bias.

翻译：本研究探讨了在假释人群中，有无行为健康问题的个体中无家可归与再犯之间的关系。研究同时阐释了一种新的暴露效应总结方法——增量倾向得分（IPS），该方法避免了犯罪学中常用其他估计方法的缺陷。我们评估了在假释队列（n=2,453）中，假释开始时无家可归对一年内再次被捕的影响。考虑受试者更可能或更不可能无家可归时的一般性及特定犯罪再犯情况，估计了IPS效应，并按精神疾病状态评估了效应的变异。我们采用双重稳健机器学习估计器，灵活且高效地估计效应。减少无家可归导致估计的平均再犯率降低。将无家可归的几率除以10，相较于乘以10，对应估计的平均再犯率降低9%（p<0.05），效果显著。较温和的干预措施显示出较小且不显著的效应量。按诊断结果和再逮捕类型分层后，结果相似且无统计学显著性。考虑到观察性数据和推广性的局限性，本研究通过新的分析方法支持无家可归作为不同人群中再犯的风险因素。减少再犯的努力应包括降低无家可归可能性的干预措施，例如增加住房获取途径。同时，确定再犯风险因素的研究应考虑IPS效应等替代效应，以最大化有效性并减少偏倚。