We're pleased to welcome Eva-Maria Oess, CTML Visiting Student Researcher, as the speaker for this week’s CTML Seminar on Wednesday, November 12th. She wil be presenting on "Heterogeneous Net Treatment Effects" joint work with Lennard Maßmann. This talk will take place at 12:00PM at Berkeley Way West, 5th Floor, Room 5401.
We introduce a novel method for estimating heterogeneous net treatment effects under unit-varying outcome and cost effects. Our approach is designed for optimal assignment of a binary treatment that induces a cost-benefit trade-off: First, it enables identification of the target population for which the treatment effect is larger than or equal to the cost effect. Second, it allows for direct prioritization of the treatment assignment via the effect size. Using a generalized random forest, we minimize a joint loss function based on the local difference between the two effects. We show that our approach targets a different splitting criterion that achieves a lower mean squared error compared to separate effect estimation and subsequent differencing, if the treatment and cost effects are correlated. In a simulation study, we confirm these findings for finite samples. Additionally, we discuss an empirical application. We use data from a large nonprofit organization to analyze the net effect of a fundraising campaign to increase pledge payments while avoiding donor attrition.