aiqbench analysis
This research addresses the fundamental limits of policy learning in digital experimentation, specifically tackling the challenge of weak signal-to-noise ratios amidst massive data volumes. By formalizing treatment-effect estimates as Gaussian observations with bounded noise, the authors prove that optimal policies are generally unlearnable but derive minimax-adaptive linear smoothers that achieve vanishing welfare regret when effects vary smoothly. The framework validates its utility through large-scale experiments at Netflix, demonstrating that personalized smoothing significantly outperforms unpersonalized baselines even in noisy environments.
Policy Learning with Weak Signals
arXiv:2610.10167v1 Announce Type: cross Abstract: 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. We establish that, in general, the optimal treatment policy is not learnable in this setting. Even learning the optimal policy value suffers from impractically slow rates. However, when treatment effects vary smoothly, we derive minimax-adaptive policies based on linear smoothers that achieve vanishing welfare regret. 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.
Abstract: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. We establish that, in general, the optimal treatment policy is not learnable in this setting. Even learning the optimal policy value suffers from impractically slow rates. However, when treatment effects vary smoothly, we derive minimax-adaptive policies based on linear smoothers that achieve vanishing welfare regret. 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.
Methodology (stat.ME); Machine Learning (cs.LG); Statistics Theory (math.ST)
arXiv:2610.10167 [stat.ME]
(or arXiv:2610.10167v1 [stat.ME] for this version)
https://doi.org/10.48550/arXiv.2610.10167 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 214 words
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