Sequential treatment assignments in online experiments lead to complex dependency structures, often rendering identification, estimation and inference over treatments a challenge. Treatments in one session (e.g., a user logging on) can have an effect that persists into subsequent sessions, leading to cumulative effects on outcomes measured at a later stage. This can render standard methods for identification and inference trivially misspecified. We propose T-Learners layered into the G-Formula for this setting, building on literature from causal machine learning and identification in sequential settings. In a simple simulation, this approach prevents decaying accuracy in the presence of carry-over effects, highlighting the importance of identification and inference strategies tailored to the nature of systems often seen in the tech domain.

翻译：在线实验中的序贯处理分配导致复杂的依赖结构，通常使得对处理效应的识别、估计与推断成为挑战。单次会话（例如用户登录）中的处理可能产生持续影响至后续会话，从而对后期测量的结果产生累积效应。这可能导致标准的识别与推断方法产生严重误设。基于因果机器学习与序贯设定识别领域的文献，我们针对此场景提出分层嵌入G公式的T-学习者方法。在简单模拟中，该方法能防止在遗留效应存在时出现精度衰减，突显了针对科技领域常见系统特性定制识别与推断策略的重要性。