Anthony Strittmatter

big data and econometrics seminar
à distance

Anthony Strittmatter

CREST
Optimal targeting in fundraising: A causal machine-learning approach
Co-écrit avec
Tobias Cagala, Ulrich Glogowsky, Johannes Rincke
Date(s)
Mardi 29 mars 2022| 14:00 - 15:30
Contact(s)

Michel Lubrano : michel.lubrano[at]univ-amu.fr
Pierre Michel : pierre.michel[at]univ-amu.fr

Résumé

Ineffective fundraising lowers the resources charities can use to provide goods. We combine a field experiment and a causal machine-learning approach to increase a charity’s fundraising effectiveness. The approach optimally targets a fundraising instrument to individuals whose expected donations exceed solicitation costs. Our results demonstrate that machine-learning-based optimal targeting allows the charity to substantially increase donations net of fundraising costs relative to uniform benchmarks in which either everybody or no one receives the gift. To that end, it (a) should direct its fundraising efforts to a subset of past donors and (b) never address individuals who were previously asked but never donated. Further, we show that the benefits of machine-learning-based optimal targeting even materialize when the charity only exploits publicly available geospatial information or applies the estimated optimal targeting rule to later fundraising campaigns conducted in similar samples. We conclude that charities not engaging in optimal targeting waste significant resources.

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