  Causal inference from observational data is the goal of many data analyses in
the health and social sciences. However, academic statistics has often frowned
upon data analyses with a causal objective. The introduction of the term "data
science" provides a historic opportunity to redefine data analysis in such a
way that it naturally accommodates causal inference from observational data.
Like others before, we organize the scientific contributions of data science
into three classes of tasks: Description, prediction, and counterfactual
prediction (which includes causal inference). An explicit classification of
data science tasks is necessary to discuss the data, assumptions, and analytics
required to successfully accomplish each task. We argue that a failure to
adequately describe the role of subject-matter expert knowledge in data
analysis is a source of widespread misunderstandings about data science.
Specifically, causal analyses typically require not only good data and
algorithms, but also domain expert knowledge. We discuss the implications for
the use of data science to guide decision-making in the real world and to train
data scientists.
