What-If Scenarios
Coming SoonTest counterfactual interventions against live simulations. Ask Shadow "what would happen if…" and receive projected outcomes with full confidence bounds — before making any changes to production.
Example Scenarios
Based on the current Yelp demo simulation, here are scenarios you could test:
What happens to first-review rates if we remove the 2 extra verification steps for iOS reviewers on new NYC listings?
This scenario will be available in the next release
Simulate emailing consumers who viewed but didn't review a newly listed restaurant within 48 hours.
This scenario will be available in the next release
Model the recovery trajectory if Android notification delivery returns to Q3 levels via an improved permission flow.
This scenario will be available in the next release
What if we add a lightweight mobile-web re-engagement path for consumers who bounce before leaving a first review?
This scenario will be available in the next release
How What-If Works
Counterfactual Branching
Fork any active simulation at a specific point in time and apply a hypothetical change. Shadow computes the divergent trajectory using the same statistical models.
Side-by-Side Comparison
Compare the baseline projection against one or more what-if scenarios. See divergence points, confidence intervals, and net impact in a unified view.
Traceable To A Layer
L1 bounds what the system makes possible, L2 computes the divergent trajectory. L3 explains the result in words when you ask — it never decides the outcome. Every scenario shows which layer produced what.
Explicit Uncertainty
Every scenario includes confidence bounds. If a what-if introduces too much uncertainty, Shadow flags it as SPECULATIVE rather than presenting false precision.