Policy Learning with Weak Signals

Key points

• We formalize this regime by modeling treatment-effect estimates from increasingly fine covariate partitions as Gaussian observations with bounded signal-to-noise ratios.1
• Even learning the optimal policy value suffers from impractically slow rates.1
• However, when treatment effects vary smoothly, we derive minimax-adaptive policies based on linear smoothers that achieve vanishing welfare regret.1
• We demonstrate the practical value of our framework by applying it to large-scale real-world experiments at Netflix, showing that personalized linear-smoothing policies can dominate unpersonalized policies even in this challenging empirical setting.1

Sources (1)

• [1]Policy Learning with Weak Signals
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 02:41 PM

  Policy learning in digital experimentation faces three challenges: weak signal-to-noise ratios, rich covariate spaces, and massive data volumes.

  We formalize this regime by modeling treatment-effect estimates from increasingly fine covariate partitions as Gaussian observations with bounded signal-to-noise ratios.

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