  Targeted Learning is a subfield of statistics that unifies advances in causal
inference, machine learning and statistical theory to help answer
scientifically impactful questions with statistical confidence. Targeted
Learning is driven by complex problems in data science and has been implemented
in a diversity of real-world scenarios: observational studies with missing
treatments and outcomes, personalized interventions, longitudinal settings with
time-varying treatment regimes, survival analysis, adaptive randomized trials,
mediation analysis, and networks of connected subjects. In contrast to the
(mis)application of restrictive modeling strategies that dominate the current
practice of statistics, Targeted Learning establishes a principled standard for
statistical estimation and inference (i.e., confidence intervals and p-values).
This multiply robust approach is accompanied by a guiding roadmap and a
burgeoning software ecosystem, both of which provide guidance on the
construction of estimators optimized to best answer the motivating question.
The roadmap of Targeted Learning emphasizes tailoring statistical procedures so
as to minimize their assumptions, carefully grounding them only in the
scientific knowledge available. The end result is a framework that honestly
reflects the uncertainty in both the background knowledge and the available
data in order to draw reliable conclusions from statistical analyses -
ultimately enhancing the reproducibility and rigor of scientific findings.
