Instruction: Outline the steps you would take to design a study that accurately measures the causal impact of a new algorithm on user engagement. Ensure your answer addresses potential pitfalls that could introduce selection bias and how you would mitigate them.
Context: This question evaluates the candidate's ability to design a rigorous study in a tech environment, focusing on causal inference principles. It tests their understanding of selection bias, their ability to anticipate and mitigate it, and their knowledge of experimental design in a real-world tech scenario.
Official answer available
Preview the opening of the answer, then unlock the full walkthrough.
Firstly, to ensure clarity and precision, let's define "user engagement" as a measurable metric. For the purpose of this study, let's consider daily active users (DAUs) as our primary metric for engagement. DAUs are calculated as the number of unique users who logged on to at least one of our platforms during a calendar day. This metric is widely recognized for its simplicity and effectiveness in capturing active participation.
Now, onto the design of the study. The gold standard for measuring causal impact in an environment like ours would be to conduct a randomized controlled trial (RCT). The key here is randomization. By randomly assigning users to either the treatment group (exposed to the new algorithm) or the control group (not exposed to the new algorithm), we can mitigate selection bias. This bias occurs when the characteristics of the two groups differ in ways that affect the outcome, in this case, user engagement....