Rethinking recidivism through a causal lens

TL;DR

This paper applies causal inference methods to analyze the effect of incarceration on recidivism, revealing that longer prison sentences may increase the likelihood of re-offending, thus challenging traditional assumptions.

Contribution

It demonstrates the use of DAG adjustment and double machine learning in criminal justice data to estimate treatment effects of incarceration on recidivism.

Findings

Incarceration has a detrimental effect on recidivism.

Longer prison sentences increase re-offending likelihood.

Causal inference methods can inform criminal justice policies.

Abstract

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.