  1.A goal of many research programs in biology is to extract meaningful
insights from large, complex data sets. Researchers in Ecology, Evolution and
Behavior (EEB) often grapple with long-term, observational data sets from which
they construct models to address fundamental questions about biology.
Similarly, epidemiologists analyze large, complex observational data sets to
understand the distribution and determinants of human health and disease. A key
difference in the analytical workflows for these two distinct areas of biology
is delineation of data analysis tasks and explicit use of causal inference
methods, widely adopted by epidemiologists. 2.Here, we review the most recent
causal inference literature and describe an analytical workflow that has direct
applications for EEB researchers. 3.The first half of this commentary defines
four distinct analytical tasks (description, prediction, association, and
causal inference), and the corresponding approaches to data analysis and model
selection. The latter half is dedicated to walking the reader through the steps
of casual inference, focusing on examples from EEB. 4.Given increasing interest
in causal inference and common misperceptions regarding the task of causal
inference, we aim to facilitate an exchange of ideas between disciplinary silos
and provide a framework for analyses of all data, though particularly relevant
for observational data.
