Basic understanding of control variables in observational studies I'd like to understand the basic logic for control variables in observational studies.
According to Wikipedia, one should control for confounding variables, which "in this context means other factors that not only influence the dependent variable (the outcome) but also influence the main independent variable."
But shouldn't one also control for factors which influence the dependent variable directly? E.g., if I have a data set of ten large marathons and want to investigate the effect of age on marathon times, don't I want to control for the day's temperature (even though this does not influence the runner's age)?

Yes, both are right. Basically, the idea is that if you omit an important variable in your analysis, then you are going to miss some predictive power. In addition, if the variable you omitted is correlated to some of your independent variables, then your estimates for the coefficients of these variables are going to be biased, and might cause trouble for the interpretation of your analysis. See here.
EDIT:
As @frank said below, if your omitted variable (temperature) is uncorrelated to your independent variable (age), then omitting it should create no bias in your estimate of the coefficient $b_\text{Age}$. However, including it could still improve the accuracy of your model, if the effect of temperature on the performance is significant. This is why I highlighted "in addition" above. Adding temperature as a predictor could be useful even if it is uncorrelated to your independent variables.
Regarding your second question, I think that the example is indeed quite strange. In this case, additional variables like "Net wealth", "Widowed?" or "Health measure" could definitely refine the measure of the effect of age on happiness. Yes, they have no causality on age, but age certainly has some forms of causality on them, potentially creating correlation (certainly in this example). So adding them as predictors would definitely be a good idea in my opinion. I think that this is because the article seems to focus on "causal models". Maybe they consider that since age changes by itself without external influence, then they want to consider that the additional effects of the variables I imagined above should be "included" in the global effect of age, since age has a causal impact on them.
Anyway, I suggest you focus on the answers @frank and I have provided for your example with marathon.
