Where to invest? ROI analysis with continuous treatments using doubly robust machine learning

Oregon Convention Center 

In sales operations, a business makes tradeoff decisions about where to invest and divest in order to optimize revenue. For instance, in advertising sales, a customer segment (e.g., by vertical) responds differently to treatments, such as the number of sales pitches about a new advertising platform. To optimize revenue impacts, the business needs to know where to make investments and divestments. We apply causal inference methods to estimate impacts of continuous treatments (i.e., dollars invested) and support tradeoff decision-making. Specifically, we implement doubly robust machine learning methods on observational sales data to (1) analyze sales treatment mechanisms and (2) estimate their impacts on revenue outcomes. Through simulation and real data analysis, we demonstrate the potential for doubly robust methods to mitigate bias in ROI decision-making in business problems about investments and resourcing.

Double robustness

ROI analysis

Machine learning

Advertising sales

Decision support 

Business and Economic Statistics Section