PyData Amsterdam 2024

Recent advancements in causal inference have led to the emergence of sophisticated targeting methods, which are perceived as intrusive by consumers. In response, policymakers have recently imposed bans on targeting due to its privacy invasive nature (e.g., Meta). In this talk, we introduce two private targeting strategies that we prove to satisfy differential privacy: a mathematical definition of privacy. These two private targeting strategies allow analysts to target customers while simultaneously establish a level of privacy risk. We first introduce "Private Causal Neural Networks" (PCNNs), which estimate the causal or incremental effect of a targeting intervention. The second strategy involves the randomization of the targeting decision. In two increasingly complex simulation studies, we benchmark the two private targeting strategies to accurately learn the population average treatment effect, conditional average treatment effect (i.e., CATE), and its targeting profitability. In a field experiment with over 400,00 customers, we empirically apply the privacy protection strategies and visualize the inherent trade-off between privacy risk and profitability.