I was going to write more about quasi-experimental methods, but then I realized why these are usually discussed last in econometrics/empirical methods books. In order to see why quasi-experimental methods are useful, it’s first helpful to understand why experiments are good and where non-experimental methods can falter. Of course, experiments have drawbacks too and non-experimental non-quasi-experimental methods can produce valid results under some conditions. But we’ll talk about all that later.
When properly designed and executed, an experiment will easily allow you to estimate the causal effect of a randomly assigned condition (“treatment”), X, on any outcome Y: effect of a job training program on employment, effect of teacher training on student outcomes, effect of a drug on mortality, effect of dog ownership on health, etc. At a very basic level, a valid experiment only requires two things: (1) a control group (let’s say one composed of people) that is not exposed to the treatment X and (2) random assignment to treatment. This kind of setup is called a “randomized controlled experiment”. In this case, you can just compare the differences in Y’s in the two groups to arrive at the causal effect of X (divide by differences in X between the two groups if X is continuous).
Why do you need a control group? Because things change over time. Over longer time scales, people age, get sick, get better, gain/lose weight, get/lose jobs, learn/forget things, move, and generally act in ways that could affect Y even without X. Over shorter time scales, people might be affected by the time of day, by the temperature, by changes in their mood, by the building into which you bring them, or even by the fact that they are taking part in an experiment. If you don’t have a control group, it’s essentially impossible to tease out the effect of X on Y from the influence of other forces on Y. Most researchers know this and use a control group to ensure that the estimated effect of X on Y is not confounded by anything else happening to the treated group.
One exception I found (there surely are others) is this study, which recruited 4-10 month old infants and mothers for a sleep lab study of “crying it out” (a method by which some parents teach babies to fall asleep on their own by letting them cry and learn to self-soothe). All mothers were instructed to let the babies “cry it out” when falling asleep, so no control group was used. Even after the babies stopped crying on the third day, their cortisol levels were elevated, suggesting that they were stressed out. As this Slate articlepoints out, it is impossible to know whether the babies were stressed out by exposure to “cry it out” (as the research article claims) or by the fact that they were in a foreign environment – the sleep lab. The absence of a control group that faced the same conditions without being exposed to “crying it out” thus fundamentally limits this study’s ability to say anything definitive about how crying it out affects stress levels.
Now you might say, “Sure, for some things, a control group that’s part of the experiment is important. But for outcomes like mortality or income, why can’t we just compare outcomes of people who enrolled in the experiment to outcomes similar people who are not part of the experiment? That seems easier and cheaper.” The problem with this approach is that it’s hard to be sure you’re comparing treated “oranges” to untreated “oranges” as opposed to treated “oranges” to untreated “apples”. Even if you collect information on hundreds of individual characteristics, it’s hard to be sure that there aren’t other characteristics that differ between your experimental treatment group and your real-world control group. And those unobserved differences might themselves influence outcomes. For example, maybe the group that signed up for your job training experiment is more (less) motivated and would have gotten jobs at higher (lower) rates than the real-world control group even if they didn’t take part in your experiment. Or maybe the experimental group is healthier (sicker) in ways that you aren’t capturing and they would have lived longer (died sooner) than the real-world control group. For these reasons, you should always be suspicious of “experiments” where the control group is non-existent or isn’t drawn from the group that signed up for the study.
Finally, why can’t you let people decide themselves whether to be in the control group or not? For the same reason that your control group needs to consist of people who signed up for your experiment – if you don’t assign people to the treatment group randomly you can’t be sure that the two groups – treatment and control – are alike in every single way that affects Y except for X. It could be that people who sign up for the treatment are more desperate for whatever reason, and desperate people may behave differently in all sorts of ways that then affect all sort of outcome. Or it could be that they are more adventurous, which again could affect them in all sorts of ways. Or they eat more broccoli/cheese/ice cream and you didn’t think to ask about that. If there are any such differences that you don’t observe andcontrol for adequately, you can never be sure that differences in Y between the two groups are solely due to the treatment X.
But what if you’re ABSOLUTELY SURE that there’s nothing different between your treatment and non-randomly selected control group that could affect Y other than X and other things you’ve controlled for? The thing is, you can never be sure, otherwise you probably wouldn’t be running an experiment. To be absolutely sure would imply that you know everything about how Y is determined except for the effect of X on Y. And there’s just no way that we know that much about anything that we’d want to study (at least as far as social science and medicine are concerned). But if you have a good counter-example, email me!
That was a long one! Next time, we’ll talk about how even randomized controlled experiments can go wrong.