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• The life expectancy example is based on Zaman (2010). The associated data are from the World Bank Indicator Tables for the year 2010, including only all observations that have no missing values.

• The smoother used by R in Figure 1.1 is called “lowess,” which stands for LOcally WEighted Scatterplot Smoother. It's used to detect nonlinearities in a scatterplot.

• The classic reference on eliciting causal information from observational data is Rosenbaum (2010), but it is a technical book written for people who have taken advanced courses in statistics. A much more accessible book that covers both experimental and observational methods is Rosenbaum (2017), which is filled with words and has practically no equations; someone who wants to understand the topic and who hasn't taken advanced courses in statistics can do no better than this book. It also contains a very good example of how observational data can be used to inform causal questions; see the section entitled “A Simple Example: Does a Parent's Occupation Put Children at Risk?” (pp. 119‐124).

• Looking only at correlations (or lack thereof) can make it impossible to uncover causal relations. Suppose a car travels at a constant speed over hilly roads. The driver will have to accelerate on the inclines and brake on the declines to maintain a constant speed. A person who knows nothing of automobiles might observe these data and conclude that depressing the accelerator or the brake has nothing to do with what speed the car travels.