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.

翻译：犯罪累犯的预测建模（即预测个人未来是否会再次犯罪）具有悠久且充满争议的历史。现代因果推断方法使我们能够超越预测，转向基于观测数据集评估特定干预对结果的“处理效应”。本文利用北卡罗来纳州的一个知名数据集，专门研究监禁（服刑时间）对累犯的影响。我们解释并演示了两种处理混杂偏倚的流行因果方法：有向无环图调整法和双机器学习法，包括对未观测混杂因素的敏感性分析。研究发现监禁对累犯具有负面效应，即更长的刑期会增加个人释放后再次犯罪的可能性，尽管该结论不应超出本研究数据的适用范围。我们希望这一案例研究能够为未来因果推断在刑事司法分析中的应用提供参考。