Reconciling Causality and Statistics

TL;DR

This paper introduces Judea Pearl's causality framework, explaining how observational data can inform causal inferences beyond traditional correlation, with practical business examples.

Contribution

It presents a clear, self-contained overview of the causality revolution, bridging the gap between statistical correlation and causal inference.

Findings

Causality can be inferred from observational data using new mathematical frameworks.

Pearl's methods provide tools for causal reasoning in real-world scenarios.

The paper demonstrates applications with concrete business examples.

Abstract

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.