Optimal Targeting in Fundraising: A Causal Machine-Learning Approach

Abstract

This paper studies optimal targeting as a means to increase fundraising efficacy. We randomly provide potential donors with an unconditional gift and use causal-machine learning techniques to "optimally" target this fundraising tool to the predicted net donors: individuals who, in expectation, give more than their solicitation costs. With this strategy, our fundraiser avoids lossy solicitations, significantly boosts available funds, and, consequently, can increase service and goods provision. Further, to realize these gains, the charity can merely rely on readily available data. We conclude that charities that refrain from fundraising targeting waste significant resources.

The Smart Green Nudge: Reducing Product Returns through Enriched Digital Footprints & Causal Machine Learning

“…Only a handful of studies apply these methods to estimate the efficacy of treatment interventions on an individual level and personalize them to account for heterogeneity. Cagala et al (2021) employ CML methods for the optimal distribution of gifts among potential donors in a fundraising campaign. Zhang and Luo (2022) rely on causal forests to understand the impact of social media postings on restaurant survival.…”

Paternalism, Autonomy, or Both? Experimental Evidence from Energy Saving Programs

“…First, many recent studies in the economics literature have explored targeting based on paternalistic or autonomous approaches. In addition to the papers cited earlier, recent studies using paternalistic assignment include Johnson, Levine, and Toffel (2020); Murakami, Shimada, Ushifusa, and Ida (2020); Cagala, Glogowsky, Rincke, and Strittmatter (2021); Christensen, Francisco, Myers, Shao, and Souza (2021); Gerarden and Yang (2021) and studies using autonomous approaches include Alatas, Purnamasari, Wai-Poi, Banerjee, Olken, and Hanna (2016); Dynarski, Libassi, Michelmore, and Owen (2018); Lieber and Lockwood (2019); Unrath (2021); Waldinger (2021). We are not aware of any existing study that builds an algorithm to identify the optimal mix of paternalistic assignment and autonomous choice.…”

Essays on causal inference

“…They find that the total volume of user-generated content and the extent to which user photos are rated as helpful have a significant positive effect on the likelihood of restaurant survival. Another study from the broader field of marketing that uses causal ML is Cagala, Glogowsky, Rincke, and Strittmatter (2021). The authors apply causal ML to determine the strategy for distributing gifts among potential donors to a fundraising campaign that maximizes expected net donations.…”