Estimating Treatment Effects and Predicting Recidivism for Community Supervision Using Survival Analysis with Instrumental Variables

Criminal justice researchers often seek to predict criminal recidivism and to estimate treatment effects for community corrections programs. Although random assignment provides a desirable avenue to estimating treatment effects, often estimation must be based on observational data from operating corrections programs. Using observational data raises the risk of selection bias. In the community corrections contexts, researchers can sometimes use judges as instrumental variables. However, the use of instrumental variable estimation is complicated for nonlinear models, and when studying criminal recidivism, researchers often choose to use survival models, which are nonlinear given right-hand-censoring or competing events. This paper discusses a procedure for estimating survival models with judges as instruments. It discusses strengths and weaknesses of this approach and demonstrates some of the estimation properties with a computer simulation. Although this paper's focus is narrow, its implications are broad. A conclusion argues that instrumental variable estimation is valuable for a broad range of topics both within and outside of criminal justice.