Tobias Cagala, Ulrich Glogowsky, Johannes Rincke, Anthony Strittmatter
arXiv 10 Mar 2021 · Econometrics · 9 citations (OpenAlex)
arXiv:2103.10251 · PDF · DOI · OpenAlex · Extracted main text
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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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | –- and Wager, S (2021) Policy Learning with Observational Data | 1.000 | 8 | 4 | 100% |
| 2 | Rosenbaum, P. and Rubin, D (1983) The Central Role of Propensity Score in Observational Studies for Causal Effects | 1.000 | 7 | 3 | 100% |
| 3 | Falk, A (2007) Gift Exchange in the Field | 1.000 | 5 | 5 | 100% |
| 4 | –- and –- (2013) Charitable Giving | 1.000 | 5 | 4 | 100% |
| 5 | Zhou, Z., Athey, S. and Wager, S (2018) Offline Multi-action Policy Learning: Generalization and Optimization | 1.000 | 5 | 4 | 100% |
| 6 | Alpizar, F., Carlsson, F. and Johansson-Stenman, O (2008) Anonymity, Reciprocity, and Conformity: Evidence from Voluntary Contributions to a National Park in Costa Rica | 0.843 | 3 | 3 | 100% |
| 7 | Athey, S. and Imbens, G. W (2019) Machine Learning Methods that Economists Should Know About | 0.843 | 3 | 3 | 100% |
| 8 | Yin, B., Li, Y. and Singh, S (2020) Coins Are Cold and Cards Are Caring: The Effect of Pregiving Incentives on Charity Perceptions, Relationship Norms, and Donation… | 0.843 | 3 | 3 | 100% |
| 9 | –-, Fernández-val, I. and Luo, Y (2018) b) | 0.811 | 4 | 2 | 100% |
| 10 | –-, Demirer, M., Duflo, E. and Fernández-val, I (2020) Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 78 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | What Is the Value Added by Using Causal Machine Learning Methods in a Welfare Experiment Evaluation? | 0.405 | 1 | 1 |