In examining the causal impact of product features on user retention, the text explores the limitations of relying solely on correlation and A/B testing, highlighting the use of causal models and the "backdoor criterion" as alternative methods. By employing techniques such as lasso regression and filtering for confounding variables, the analysis seeks to establish a clearer causal relationship between running specific queries and user retention. The document illustrates how causal diagrams can clarify hypotheses and identify confounders, emphasizing the importance of distinguishing between confounding and mediator variables to avoid misleading conclusions. The analysis revealed that running a "users query" may act as an "aha moment" that enhances retention by encouraging further query activity, underscoring the nuanced interpretation required when assessing causal impacts in product analytics.