Abstract
In Chaps. 4–8, we showed how multi-predictor regression can be used to control for confounding in observational data, with the purpose of estimating the independent association of an exposure with an outcome. The cautious language of associations notwithstanding, the underlying purpose is often to quantify causal relationships. In this chapter, we explain what is meant by the average causal effect of an exposure, and discuss the conditions under which regression might be able to estimate it. We also show the extra steps that are needed to estimate marginal effects, which sometimes differ from the conditional effects that regression models estimate by default.
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© 2012 Springer Science+Business Media, LLC
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Vittinghoff, E., Glidden, D.V., Shiboski, S.C., McCulloch, C.E. (2012). Strengthening Causal Inference. In: Regression Methods in Biostatistics. Statistics for Biology and Health. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-1353-0_9
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DOI: https://doi.org/10.1007/978-1-4614-1353-0_9
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Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4614-1352-3
Online ISBN: 978-1-4614-1353-0
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