A Second Chance to Get Causal Inference Right: A Classification of Data Science Tasks

By Miguel A. Hern'an, J. Hsu, B. Healy. CHANCE : New Directions for Statistics and Computing, 2018

412 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, History and Philosophy of Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/590ead4aeddbf8fea8414998b2dc3b74576a71cb

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In Artificial Intelligence

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Citations in the corpus

Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations (2020, background): “Estimating causal effects is a key aim of applied health research.(1) One approach is to conduct a randomized controlled experiment, but practical and ethical constraints mean this is only possible for a limited range of exposures.”
CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories (2021, background): “Hernan et al. [49] discuss how data science can tackle causal inference from data by considering it as a new kind of data science task known as counterfactual prediction .”