Policy Learning with Adaptively Collected Data

Understand

Learning optimal policies from historical data enables personalization in a wide variety of applications including healthcare, digital recommendations, and online education.

• The growing policy learning literature focuses on settings where the data collection rule stays fixed throughout the experiment.
• However, adaptive data collection is becoming more common in practice, from two primary sources: 1) data collected from adaptive experiments that are designed to improve inferential efficiency; 2) data collected from production systems that progressively evolve an operational policy to improve performance over time (e.g.
• contextual bandits).