通过因果视角重新思考累犯问题
Rethinking recidivism through a causal lens
AI总结：
本文以北卡罗来纳州数据为基础，采用DAG调整、DML及敏感性分析评估监禁对累犯的因果效应，发现更长刑期会增加再犯罪可能。
摘要：
刑事累犯（即人们未来是否会再次犯罪）的预测建模有着漫长且充满争议的历史。现代因果推断方法使我们能够超越预测，并在观察性数据集中聚焦于特定干预措施对结果的“处理效应”。本文使用来自北卡罗来纳州的一个知名数据集，专门考察监禁（监狱服刑时间）对累犯的影响。文中解释并演示了两种用于处理混杂偏差的常用因果方法：有向无环图（DAG）调整和双重机器学习（DML），其中还包括针对未观测混杂因素的敏感性分析。我们发现，监禁对累犯具有不利影响，也就是说，更长的刑期会使个人在获释后更有可能再次犯罪，不过这一结论不应被推广到我们的数据范围之外。我们希望本案例研究能够为未来将因果推断应用于刑事司法分析提供参考。
英文摘要：
Predictive modeling of criminal recidivism, or whether people will re-offend in the future, has a long and contentious history. Modern causal inference methods allow us to move beyond prediction and target the "treatment effect" of a specific intervention on an outcome in an observational dataset. In this paper, we look specifically at the effect of incarceration (prison time) on recidivism, using a well-known dataset from North Carolina. Two popular causal methods for addressing confounding bias are explained and demonstrated: directed acyclic graph (DAG) adjustment and double machine learning (DML), including a sensitivity analysis for unobserved confounders. We find that incarceration has a detrimental effect on recidivism, i.e., longer prison sentences make it more likely that individuals will re-offend after release, although this conclusion should not be generalized beyond the scope of our data. We hope that this case study can inform future applications of causal inference to criminal justice analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
