(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
Danielle Fumia writes:
I am a research at the Washington State Institute for Public Policy, and I work on research estimating the effect of college attendance on earnings. Many studies that examine the effect of attending college on earnings control for college degree receipt and work experience. These models seem to violate the practice you discuss in your data analysis book of not including intermediate outcomes in the regression of the treatment (attending college) on y (earnings). However, I’m not sure if this situation is different because only college attendees can obtain a degree, but in any case, this restriction wouldn’t be true for work experience. I fear I am missing an important idea because most studies control for one or both seemingly intermediate outcomes, and I cannot seem to find an explanation after much research.
My reply:
For causal inference, I recommend that, instead of starting by thinking of the outcome, you start by thinking of the intervention. As always, the question with the intervention is “Compared to what?” You want to compare people who attended college to equivalent people who did not attend college. At that point you can think about all the outcomes that flow from this choice. In your regressions, you can control for things that came before the choice, but not after. So you can control for work experience before the forking of paths (college or no college) but not for work experience after that choice. Also, suppose your choice is at age 21: either a student attends college by age 21 or he or she does not. Then that’s the fork. If a non-attender later goes to college at age 25, he or she would still be in the “no college attendance at 21” path.
I’m not saying you have to define college attendance in that way, I’m just saying you have to define it in some way. If you don’t define the treatment, your analysis will (implicitly) define it for you.
Here’s more you can read on causal inference and regression, from my book with Jennifer. We go into these and other points in more detail.
The post When doing causal inference, define your treatment decision and then consider the consequences that flow from it appeared first on Statistical Modeling, Causal Inference, and Social Science.
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