IV estimation for a ride‑sharing feature when A/B testing is infeasible due to interference

Context

You need to estimate the causal effect of a new ride‑sharing feature on trip volume. A clean A/B test is not feasible because users (drivers/riders) interact within a marketplace, creating interference/spillovers across units (e.g., one driver's treatment can affect other drivers' and riders' outcomes in the same market/time).

Task

• Propose an instrumental‑variables (IV) strategy to identify the causal effect of feature exposure/adoption on trip volume in the presence of interference.
• Clearly define the unit of analysis, treatment, outcome, and the level at which interference is addressed (e.g., market × time clustering/aggregation).
• State and justify all IV assumptions precisely:
  1. Relevance
  2. Exclusion restriction
  3. Independence (as‑if random)
  4. Monotonicity (if you claim a LATE interpretation)
• Provide at least two concrete, plausibly exogenous instruments and justify them. Examples to consider include:
  • Staggered driver app version eligibility (e.g., forced update schedule, app‑store review lags)
  • An encouragement design (e.g., hash‑bucket canary eligibility) or a weather‑based interaction that shifts usage only among eligibles
• Write the first‑stage and second‑stage (2SLS) equations, including controls and fixed effects.
• Describe how you will:
  • Diagnose weak instruments (first‑stage F‑statistics; Kleibergen‑Paap for robust/clustered settings)
  • Run over‑identification tests (Sargan/Hansen J)
  • Handle heteroskedasticity and clustering (e.g., two‑way clustering by market and time; wild cluster bootstrap if few clusters)
  • Assess and mitigate violations of exclusion in the presence of marketplace spillovers
• Discuss whether an effectively unlimited supply environment makes the exclusion restriction more or less credible, and why.
• If assumptions partially fail, outline sensitivity analyses or bounds (e.g., Conley‑type plausibly exogenous bounds, Anderson‑Rubin tests).