Rider Incentive Targeting: Causal Incrementality, ROI, and Spillovers

Context: You plan a rider‑side incentive (e.g., “20% off up to $10”) targeted by a propensity model. You must estimate causal incrementality and ROI in a two‑sided marketplace with selection and spillovers.

Do the following:

1) Marketplace‑Health Metrics (with formulas)

Define and provide formulas for:

• Matching quality: pickup ETA p50, post‑dispatch cancel rate within 5 minutes, driver idle minutes/trip.
• Demand: incremental trips, GMV lift.
• Supply: acceptance rate, earnings/hour.
• Composite quality‑of‑match metric: distance‑to‑pickup p50.

2) Identification Under Three Scenarios

Propose identification strategies for:

• (A) Randomized geo holdouts.
• (B) Thresholded offer scores enabling regression discontinuity: specify bandwidth selection, continuity checks, McCrary test for manipulation, and polynomial order.
• (C) Business‑rule targeting enabling IV via an operational friction instrument: state relevance and exclusion, and list threats you will test.

3) ROI Formula and Estimation

Write the ROI formula including cannibalization, subsidy burn, surge/ETA externalities, and habit formation. Specify how to estimate LTV lift and amortize CAC. Include confidence intervals via the delta method or bootstrap.

4) Two‑Stage Randomization for Spillovers

Design a two‑stage randomization (city×week, then rider) to identify direct vs spillover effects, and define estimands for total, direct, and indirect effects.

5) Multiple Testing

Choose and justify a correction strategy (Bonferroni vs BH vs hierarchical testing) for: 1 primary, 3 key secondaries, and ~20 diagnostics.

6) Heterogeneity

Outline a pre‑registered plan to detect effect moderation by city tier and weather using causal forests or group‑wise models while controlling Type‑S/M errors.

7) Diagnostics and Decisioning

Enumerate pre‑trend checks, placebo windows, and negative‑control outcomes. Define criteria for declaring success and for rollback.