Policy Learning with Weak Signals
Learning optimal treatment policies in digital experiments is generally impossible due to weak signals and massive data. However, if treatment effects vary smoothly across different groups, simple linear smoothers can achieve minimax optimality by sharing information between neighboring covariate cells.