NCJ Number
252089
Date Published
May 2017
Length
5 pages
Annotation
In order to estimate treatment heterogeneity in two randomized controlled trials of a youth summer jobs program, this study used Wager and Athey's (2015) causal forest algorithm.
Abstract
This article presents a step-by-step explanation for applied researchers regarding how the algorithm predicts treatment effects based on observables. It then explores how useful the predicted heterogeneity is in practice by testing whether youth with larger predicted treatment effects actually respond more in a hold-out sample. The application highlights some limitations of the causal forest, but it also suggests that the method can identify treatment heterogeneity for some outcomes that more standard interaction approaches would have missed. (Publisher abstract modified)
Date Published: May 1, 2017
Downloads
Similar Publications
- School Shootings and Suicide: A Comparison of School Shooters Who Die by Suicide and Non-Suicide School Shooters
- Childhood Maltreatment and Biological Aging in Middle Adulthood: The Role of Psychiatric Symptoms
- The Impact of a Youth-focused Problem-oriented Policing Initiative on Crime: Findings from a Randomized Controlled Trial in Three Cities