  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 hundred
years, and yet statistical concepts and developments that form the area of
causal inference are only decades old. In this paper, we review 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.
