Designers of content distribution networks (CDNs) often need to determine how changes to infrastructure deployment and configuration affect service response times when they deploy a new data center, change ISP peering, or change the mapping of clients to servers. Today, the designers use coarse, back-of-the-envelope calculations or costly field deployments; they need better ways to evaluate the effects of such hypothetical “what-if” questions before the actual deployments. This paper presents What-If Scenario Evaluator (WISE), a tool that predicts the effects of possible configuration and deployment changes in content distribution networks. WISE makes three contributions: 1) an algorithm that uses traces from existing deployments to learn causality among factors that affect service response time distributions; 2) an algorithm that uses the learned causal structure to estimate a dataset that is representative of the hypothetical scenario that a designer may wish to evaluate, and uses these datasets to predict hypothetical response-time distributions; 3) a scenario specification language that allows a network designer to easily express hypothetical deployment scenarios without being cognizant of the dependencies between variables that affect service response times. Our evaluation, both in a controlled setting and in a real-world field deployment on a large, global CDN, shows that WISE can quickly and accurately predict service response-time distributions for many practical what-if scenarios.