Online controlled experiments, often referred to as A/B tests, are extensively conducted by major technology companies to evaluate the effectiveness of product strategies and inform product decision-making. The sampling process in A/B tests is not instantaneous; subjects, such as users of online platforms, arrive at the platform over time and are recruited continuously throughout the experiment. This ongoing nature of sampling can lead to shifts in sample characteristics over the experimental duration, raising issues of external validity. In other words, the causal findings derived from an experiment of a particular duration may not be applicable to the target population, potentially biasing decision-making.

Randomized controlled trials (RCTs), also known as A/B tests, have become the gold standard for evaluating the effectiveness of product changes on digital platforms. Accurately estimating the effects of long-term treatments still remains a challenge. Product updates such as new user interfaces or recommendation algorithms are intended to persist in the system for an extended period. However, A/B testing is typically conducted for short durations, often less than two weeks, to facilitate rapid product iterations. Conducting lengthy experiments to capture the long-term impact of product changes becomes impractical due to potential negative impacts on user experiences, high opportunity costs associated with user traffic, and delays in decision-making processes.