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
- Racial and Ethnic Disparities in the Processing of Delinquency Cases, 2005-2022
- Audit of the Office of Justice Programs Victim Assistance Funds Subawarded by the Oregon Department of Justice to J Bar J Youth Services, Bend, Oregon
- Audit of the Office of Justice Programs Victim Assistance Funds Subawarded by the Minnesota Department of Public Safety Office of Justice Programs to Women of Nations, Incorporated, Saint Paul Minnesota