Supervised Learning Objectives for High-Dimensional Treatment Effects
The paper recasts estimating average potential outcomes for high-dimensional treatments, like text strings, as a learning problem called Causal Risk Minimization (CRM). It shows that estimation errors decompose into moment-balancing errors and introduces methods to learn stabilized weights to minimize these errors. Furthermore, it demonstrates how to project the effect of a complex treatment onto simpler attributes without retraining separate models.