Start by defining the causal question and the primary metric (e.g., driver supply shortage resolution rate), then design a driver-level randomized experiment with careful consideration of spillover effects. Use techniques like cluster randomization or spatial separation to mitigate interference, and analyze with methods that account for spillover, such as difference-in-differences or instrumental variables, to establish causality.
Pro tip: In marketplace experiments, spillover is the norm, not the exception. Proactively address it by randomizing at a level where interference is minimized (e.g., airport clusters) and measure both direct and indirect effects to avoid underestimating the treatment effect.
Clearly state the treatment (surge pricing push notifications) and the outcome (driver supply shortage resolution). Choose primary metrics like time-to-resolve shortage, number of drivers responding, and secondary metrics like driver earnings and rider wait times.
Randomize at the driver level, but account for spillover by using cluster randomization (e.g., by airport or time block) or by measuring spillover explicitly. Consider a two-sided experiment where control drivers are also monitored for changes due to treatment drivers' behavior.
Ensure proper randomization, sample size calculation, and power analysis. Monitor for compliance and potential interference during the experiment, and collect data on driver responses and supply metrics.
Use difference-in-differences, instrumental variables, or causal forests to estimate the treatment effect while adjusting for spillover. Compare treated vs. control groups and assess heterogeneity in treatment effects.
Check for robustness, conduct sensitivity analyses, and ensure that the results are not driven by confounding factors. Interpret the causal impact in the context of business goals and potential policy implications.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.