Design a causal evaluation without A/B testing

A high-impact feature can't be put through an A/B test because of policy or infrastructure constraints, but leadership still needs a go/no-go decision. Lay out a full analysis plan using Synthetic Control, or justify an alternative. Specify: (a) how the treated unit and donor pool are constructed, including eligibility and exclusion rules that prevent contamination or anticipation; (b) the pre-intervention window length and how you would handle seasonality, holidays, macro shocks, and data latency; (c) the primary outcome and guardrail metrics, with pre-registered success thresholds and a decision rubric; (d) which predictors to include (outcome lags versus covariates), the weight constraints, and how you would tune hyperparameters; (e) the diagnostics you would require before trusting the effects (pre-period RMSPE targets, in-space and in-time placebo tests, pre/post fit plots), plus your inference approach (MSPE ratio/permutation tests, uncertainty bands for pointwise and cumulative effects); (f) the sensitivity analyses (leave-one-out donors, alternative windows, augmented/regularized SCM, donor reweighting) and how you would bound spillovers; (g) the heterogeneity and persistence analyses (subgroups, dynamic effects); (h) what you would do if the pre-period fit is poor or donors are scarce; and (i) how the results map to a staged launch, rollback criteria, and post-launch monitoring.

Overview: The question assesses a data scientist's skill in causal inference, time-series analysis, and synthetic control methodology for non-randomized experiments, covering donor pool construction, pre/post diagnostics, and sensitivity and heterogeneity assessments.