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52 Chapter 1 Economic Questions and Data

of that action. Touching a hot stove causes you to get burned; drinking water
causes you to be less thirsty; putting air in your tires causes them to inflate; putting
fertilizer on your tomato plants causes them to produce more tomatoes. Causality
means that a specific action (applying fertilizer) leads to a specific, measurable
consequence (more tomatoes).

Estimation of Causal Effects

How best might we measure the causal effect on tomato yield (measured in kilo-
grams) of applying a certain amount of fertilizer, say 100 grams of fertilizer per
square meter?

One way to measure this causal effect is to conduct an experiment. In that
experiment, a horticultural researcher plants many plots of tomatoes. Each plot
is tended identically, with one exception: Some plots get 100 grams of fertilizer
per square meter, while the rest get none. Moreover, whether a plot is fertilized
or not is determined randomly by a computer, ensuring that any other differences
between the plots are unrelated to whether they receive fertilizer. At the end of
the growing season, the horticulturalist weighs the harvest from each plot. The
difference between the average yield per square meter of the treated and
untreated plots is the effect on tomato production of the fertilizer treatment.

This is an example of a randomized controlled experiment. It is controlled in
the sense that there are both a control group that receives no treatment (no fertil-
izer) and a treatment group that receives the treatment (100 g/m2 of fertilizer). It
is randomized in the sense that the treatment is assigned randomly. This random
assignment eliminates the possibility of a systematic relationship between, for
example, how sunny the plot is and whether it receives fertilizer so that the only
systematic difference between the treatment and control groups is the treatment.
If this experiment is properly implemented on a large enough scale, then it will
yield an estimate of the causal effect on the outcome of interest (tomato produc-
tion) of the treatment (applying 100 g/m2 of fertilizer).

In this book, the causal effect is defined to be the effect on an outcome of a
given action or treatment, as measured in an ideal randomized controlled experi-
ment. In such an experiment, the only systematic reason for differences in out-
comes between the treatment and control groups is the treatment itself.

It is possible to imagine an ideal randomized controlled experiment to answer
each of the first three questions in Section 1.1. For example, to study class size,
one can imagine randomly assigning “treatments” of different class sizes to differ-
ent groups of students. If the experiment is designed and executed so that the only
systematic difference between the groups of students is their class size, then in