  We review the most important statistical ideas of the past half century,
which we categorize as: counterfactual causal inference, bootstrapping and
simulation-based inference, overparameterized models and regularization,
Bayesian multilevel models, generic computation algorithms, adaptive decision
analysis, robust inference, and exploratory data analysis. We discuss key
contributions in these subfields, how they relate to modern computing and big
data, and how they might be developed and extended in future decades. The goal
of this article is to provoke thought and discussion regarding the larger
themes of research in statistics and data science.
