text: 61,82,993144530571493378,3433516535,Miguel Hernán,"Data scientists define their work as “gaining insights” or “extracting meaning” from data. That is way too vague.  We propose that the contributions of #datascience can be organized into 3 classes of tasks: 1. description 2. prediction 3. causal inference <LINK> <LINK> @chrisbboyer Thanks, Christopher. Mullainathan & Speiss do a terrific job at explaining how machine learning works in simple terms. They also explain the distinction between prediction and causal inference, though that is not the main objective of their paper. <LINK> @kerinalthoff Several epidemiologists, biostatisticians (like Vittinghoff et al.), econometricians... have proposed a similar classification. We are looking for papers or textbooks that explicitly define these 3 categories. Suggestions welcome. The older the references, the better.",https://arxiv.org/abs/1804.10846,"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. ","Data science is science's second chance to get causal inference right: A