  The estimand framework proposed by ICH in 2017 has brought fundamental
changes in the pharmaceutical industry. It clearly describes how a treatment
effect in a clinical question should be precisely defined and estimated,
through attributes including treatments, endpoints and intercurrent events.
However, ideas around the estimand framework are commonly in text, and
different interpretations on this framework may exist. This article aims to
interpret the estimand framework through its underlying theories, the causal
inference framework based on potential outcomes. The statistical origin and
formula of an estimand is given through the causal inference framework, with
all attributes translated into statistical terms. How five strategies proposed
by ICH to analyze intercurrent events are incorporated in the statistical
formula of an estimand is described, and a new strategy to analyze intercurrent
events is also suggested. The roles of target populations and analysis sets in
the estimand framework are compared and discussed based on the statistical
formula of an estimand. This article recommends continuing study of causal
inference theories behind the estimand framework and improving the estimand
framework with greater methodological comprehensibility and availability.
