Causal inference in a "Big Data" World

At increasing velocity, volume and variety, we are generating, recording, and storing unprecedented amounts of data. Along with its many challenges and complexities, "Big Data" present exciting opportunities to better understand risk factors, to build improved predictors, and to examine the causal relationships between variables. Still, there are many sources of association between variables, including direct effects, indirect effects, measured confounding, unmeasured confounding, and selection bias. Methods to delineate causation from correlation are perhaps more pressing now than ever.

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© 2019 Laura B. Balzer