Evaluate concession gift-card policy with DID

A concession program was piloted in some regions: whenever a shipment arrives lost or damaged, the affected customer is given a gift card in place of a plain refund or replacement. Assess how the program performed. Start by pinning down the objectives so you can choose the right outcomes (for example, star ratings, complaint rates, repeat purchase, churn, CSAT, NPS, and cost per incident). Take stock of what data you have, settle the unit of analysis (customer versus city), and check whether any pre-pilot data exist. Lay out a difference-in-differences design with staggered adoption: state the regression (a treatment indicator multiplied by a post period, unit and time fixed effects, and covariates), the clustering you would use, and an event study to inspect pre-trends. Should parallel trends not hold, suggest fixes along with selection-on-observables options (propensity-score-matched DID with common support and balance checks) or a synthetic control built at the city level, and explain when each one fits. Cover guardrail metrics (refund costs, abuse, customer service load), heterogeneity (severity, item price), and the cost-benefit picture. If an RCT were on the table, spell out its design (unit, randomization, stratification, sample size and power, spillover controls) and set stop/go decision rules.

Overview: This question tests a data scientist's grasp of causal inference and experimentation design, covering difference-in-differences with staggered adoption and event-study diagnostics, data inventory and unit-of-analysis specification, clustering choices, heterogeneity and guardrail analyses, and cost-benefit plus an optional randomized experiment design. It shows up often in Analytics & Experimentation interviews because it probes whether a candidate can justify an identification strategy, spot and repair parallel-trends violations, and turn empirical findings into business-impact metrics, which places it where conceptual knowledge meets hands-on practice in the Data Science field.