  Statisticians have warned us since the early days of their discipline that
experimental correlation between two observations by no means implies the
existence of a causal relation. The question about what clues exist in
observational data that could informs us about the existence of such causal
relations is nevertheless more that legitimate. It lies actually at the root of
any scientific endeavor. For decades however the only accepted method among
statisticians to elucidate causal relationships was the so called Randomized
Controlled Trial. Besides this notorious exception causality questions remained
largely taboo for many. One reason for this state of affairs was the lack of an
appropriate mathematical framework to formulate such questions in an
unambiguous way. Fortunately thinks have changed these last years with the
advent of the so called Causality Revolution initiated by Judea Pearl and
coworkers. The aim of this pedagogical paper is to present their ideas and
methods in a compact and self-contained fashion with concrete business examples
as illustrations.
