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Predictive modeling of criminal recidivism, or whether people will re-offendin the future, has a long and contentious history. Modern causal inferencemethods allow us to move beyond prediction and target the "treatment effect" ofa specific intervention on an outcome in an observational dataset. In thispaper, we look specifically at the effect of incarceration (prison time) onrecidivism, using a well-known dataset from North Carolina. Two popular causalmethods 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 thatincarceration has a detrimental effect on recidivism, i.e., longer prisonsentences make it more likely that individuals will re-offend after release,although this conclusion should not be generalized beyond the scope of ourdata. We hope that this case study can inform future applications of causalinference to criminal justice analysis.

Rethinking recidivism through a causal lens