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Causal Models and Learning from Data: Integrating Causal Modeling and Statistical Estimation

Petersen, Maya L.; van der Laan, Mark J.

doi: 10.1097/EDE.0000000000000078

The practice of epidemiology requires asking causal questions. Formal frameworks for causal inference developed over the past decades have the potential to improve the rigor of this process. However, the appropriate role for formal causal thinking in applied epidemiology remains a matter of debate. We argue that a formal causal framework can help in designing a statistical analysis that comes as close as possible to answering the motivating causal question, while making clear what assumptions are required to endow the resulting estimates with a causal interpretation. A systematic approach for the integration of causal modeling with statistical estimation is presented. We highlight some common points of confusion that occur when causal modeling techniques are applied in practice and provide a broad overview on the types of questions that a causal framework can help to address. Our aims are to argue for the utility of formal causal thinking, to clarify what causal models can and cannot do, and to provide an accessible introduction to the flexible and powerful tools provided by causal models.

From the Divisions of Biostatistics and Epidemiology, University of California, Berkeley, School of Public Health, Berkeley, CA.

The authors report no conflicts of interest. M.L.P. is a recipient of a Doris Duke Clinical Scientist Development Award. M.J.v.d.L. is supported by NIH award R01 AI074345.

Correspondence: Maya L. Petersen, University of California, Berkeley, 101 Haviland Hall, Berkeley, CA 94720-7358. E-mail:

© 2014 by Lippincott Williams & Wilkins, Inc