Establish causality: commute playlist and driving speed

A legal concern arises that when users play a "Commute" playlist through a mobile app, they tend to drive at higher speeds. In the role of the data scientist:

a) Specify the population, the unit of analysis (for instance, a user-trip), the treatment or exposure (whether the playlist was displayed versus actually played), and the outcome (mean miles per hour), each with exact time windows.

b) Enumerate at least five significant confounders and describe how each would be measured.

c) Design a safe randomized controlled trial covering eligibility, gating, safety protections such as kill switches, metrics, and stopping criteria.

d) When a randomized trial cannot be run, lay out quasi-experimental approaches — within-driver fixed effects, difference-in-differences with staggered adoption, using exogenous changes in surfacing as an instrument, and regression discontinuity at a ranking-score cutoff — along with the assumptions that identify each and tests that could falsify them.

e) Address treatment that varies over time (the playlist begins partway through a trip), incomplete compliance, and people who are not drivers; state the level of data granularity needed to prevent leakage.

Overview: The question assesses a candidate's skill in causal inference, experiment design, measurement, and operational safety within product analytics, covering exact definitions of population and unit of analysis, treatment/exposure and outcome windows, identification of confounders, and treatment of time-varying treatment and compliance.