Abstract
A central question in statistical data analysis is when and how one can draw causal conclusions. In the typical setting, we want to ascertain how a covariate X influences an outcome Y . However, we also want to be certain that our statistical model represents a true causal process and not just some observed and possibly spurious correlation. A common example is the strong correlation between the number of storks sighted and the number of births in a country, which obviously is related to the size of the country in question, see Hunter et al. (1978, Statistics for experimenters, Wiley, NJ). Other examples also spring to mind, such as the strong correlation between shoe size and reading ability in children (the older children have larger feet have better reading ability). Unfortunately, not all spurious associations are this obvious. Thus, it is imperative that we keep causal effects in mind when analysing data and develop methods that allow us to identify “real” correlations and not spurious ones.
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Kauermann, G., Küchenhoff, H., Heumann, C. (2021). Experiments and Causality. In: Statistical Foundations, Reasoning and Inference. Springer Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-69827-0_12
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