Reconciling Causality and Statistics
It provides a pedagogical introduction to causal inference for statisticians and researchers, but is incremental as it synthesizes existing work.
This paper addresses the challenge of inferring causal relationships from observational data, which has historically been taboo in statistics due to a lack of a mathematical framework. It presents the ideas and methods of the Causality Revolution initiated by Judea Pearl in a compact, self-contained format with business examples.
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.