Causal inference: Critical developments, past and future

Causality is a subject of philosophical debate and a central scientific issue with a long history. In the statistical domain, the study of cause and effect based on the notion of fairness in comparisons dates back several hundreds of years, yet statistical concepts and developments that form the area of causal inference are only decades old. In this article, we review the core tenets and methods of causal inference and key developments in the history of the field. We highlight connections with traditional associational statistical methods, including estimating equations and semiparametric theory, and point to current topics of active research in this crucial area of our field.

Automated summary

This article reviews the core principles and methods of causal inference and important developments in the field, highlighting connections with traditional associational statistical methods.