  Information flow analysis has largely ignored the setting where the analyst
has neither control over nor a complete model of the analyzed system. We
formalize such limited information flow analyses and study an instance of it:
detecting the usage of data by websites. We prove that these problems are ones
of causal inference. Leveraging this connection, we push beyond traditional
information flow analysis to provide a systematic methodology based on
experimental science and statistical analysis. Our methodology allows us to
systematize prior works in the area viewing them as instances of a general
approach. Our systematic study leads to practical advice for improving work on
detecting data usage, a previously unformalized area. We illustrate these
concepts with a series of experiments collecting data on the use of information
by websites, which we statistically analyze.
