发表机构
Netflix（网飞）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对治疗效应随潜在类别变化的问题，用隐马尔可夫模型（HMM）推断用户情绪作为治疗效应修正因子，提出奈曼正交估计量，在Netflix A/B测试中验证了不同用户群体的推荐模型效应差异。
AI 中文摘要
许多治疗的效应可能会因潜在类别而变化，例如个性化视频推荐的效应对寻求新发现的用户可能强于寻求重看熟悉内容的用户。在数字平台的用户旅程语境中，可将用户情绪概念化为动态潜在状态，其会产生不同的行动分布。由于情绪不可观测，我们使用隐马尔可夫模型（HMM）从用户的治疗前行动中推断情绪，随后将该潜在状态作为可解释的治疗效应修正因子来评估实验结果。在治疗效应充分性假设下，每个可观测的条件效应是潜在效应的已知混合，因此当状态后验足够异质时，可识别出特定状态的效应。我们提出一种对拟合HMM误差具有奈曼正交性的估计量，支持标准影响函数置信区间。在Netflix一项新推荐模型的真实大规模A/B测试中，我们的潜在状态分析显示，高参与度用户在所有治疗变体中均表现出显著正效应，而低活跃及重看导向用户的获益证据有限。
英文摘要
Many treatments have effects that plausibly vary by latent classes. For example, the effect of personalized video recommendations may be stronger for users who seek a new discovery than for users seeking to rewatch something familiar. In the context of user journeys on digital platforms, we can conceptualize user moods as dynamic latent states that lead to distinct action distributions. Because mood is unobserved, we infer it from a user's pre-treatment actions using a hidden Markov model (HMM), and then leverage the latent state as an interpretable treatment effect modifier when evaluating experiment results. Under a treatment-effect sufficiency assumption, each observable conditional effect is a known mixture of the latent effects, so state-specific effects are identified when state posteriors are sufficiently heterogeneous. We propose an estimator that is Neyman-orthogonal to errors in the fitted HMM, enabling standard influence-function confidence intervals. In a real-world large-scale A/B test of a new recommendation model at Netflix, our latent-state analysis reveals that heavy-engagement users show significantly positive effects across all treatment variants, with limited evidence of benefit among less-active and rewatch-oriented users.
