  What-if analysis can be used as a process in data-driven decision making to
inspect the behavior of a complex system under some given hypothesis. We
propose a What-If Machine that creates hypothetical realities by resampling the
data distribution and comparing it to the an alternate baseline to measure the
impact on a target metric. Our What-If Machine enables both a method to
confirm/reject manually developed intuitions of practitioners as well as give
high-impact insights on a target metric automatically. This can support
data-informed decision making by using historical data to infer future
possibilities. Our method is not bound by a specific use-case and can be used
on any tabular data. Compared to previous work, our work enables real-time
analysis and gives insights into areas with high impact on the target metric
automatically, moving beyond human intuitions to provide data-driven insights.
