A data science team at a large e-commerce platform implemented an A/B test to measure the treatment effect of a new personalized recommendation engine (Group B) compared to the existing engine (Group A). The primary success metric is the average revenue per user (ARPU). After two weeks, the results show a statistically significant lift in ARPU for Group B (p-value = 0.03). However, a senior data scientist raises a concern about the validity of the causal conclusion due to potential network effects, as users can see and purchase items from friends' public activity feeds. Which of the following is the most significant threat to the A/B test's validity described in this scenario?
Regression to the mean
Violation of the Stable Unit Treatment Value Assumption (SUTVA)
The correct answer is the violation of the Stable Unit Treatment Value Assumption (SUTVA). SUTVA is a critical assumption for causal inference in A/B tests, and it has two main components: 1) no interference, and 2) no hidden variations of treatment. The scenario described, where users can interact and influence each other's purchasing decisions across groups, is a direct violation of the 'no interference' component. This interference, also known as a spillover or network effect, means that the treatment applied to Group B is affecting the outcomes of Group A. As a result, the measured treatment effect is likely biased because the control group's behavior is contaminated by the treatment, making it difficult to isolate the true causal impact of the new recommendation engine.
The novelty effect is an incorrect choice because it refers to users' temporary change in behavior due to the newness of a feature, not due to interactions between users. While it is a real threat to A/B tests, it is not the specific issue highlighted by the presence of network effects in the scenario.
Insufficient statistical power is incorrect. A statistically significant result (p < 0.05) indicates that the test had enough power to detect an effect of the observed magnitude. Insufficient power would more likely lead to a non-significant result (a Type II error).
Regression to the mean is also incorrect. This phenomenon occurs when subjects are selected for a study based on extreme initial measurements. In this scenario, users were randomly assigned to groups, not selected because of extremely high or low ARPU, making regression to the mean an unlikely primary threat.
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What is the Stable Unit Treatment Value Assumption (SUTVA)?
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