Home > Publications . Search All . Browse All . Country . Browse PSC Pubs . PSC Report Series

PSC In The News

RSS Feed icon

Shaefer says complex reasons for poverty make solutions challenging

Anderson discusses excess deaths under Stalin with BBC

More Fulbright Scholars from U-M than from any other research university in the US

More News

Highlights

Apply by 2/23 for Weinberg Population, Development & Climate Change funding

Needham, Hicken, Mitchell and colleagues link maternal social disadvantage and newborn telomere length

New Investigator Mentoring Program. Applications due Mar 1

PSC launches new program to support population scientists across U-M

More Highlights

Next Brown Bag

Mon, March 5, 2018, noon: Judith Seltzer on Family Complexity

Using Causal Forests to Predict Treatment Heterogeneity: An Application to Summer Jobs

Publication Abstract

Davis, Jonathan, and Sara Heller. 2017. "Using Causal Forests to Predict Treatment Heterogeneity: An Application to Summer Jobs." American Economic Review, 107(5): 546-550.

To estimate treatment heterogeneity in two randomized controlled trials of a youth summer jobs program, we implement Wager and Athey's (2015) causal forest algorithm. We provide a step-by-step explanation targeted at applied researchers of how the algorithm predicts treatment effects based on observables. We then explore 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. Our 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.

DOI:10.1257/aer.p20171000 (Full Text)

Public Access Link

Browse | Search : All Pubs