by In search of identification — instrumental variables We need relevance and validity. How realistic is validity, anyway? We ideally want our instrument to behave just like randomization in an experiment. But in the real world, how likely is that to actually happen? Or, if it’s an IV that requires control variables to be valid, how confident can we be that the controls really do everything we need them to? In the long-ago times, researchers were happy to use instruments without thinking too hard about validity. If you go back to the 1970s or 1980s you can find people using things like parental education as an instrument for your own (surely your parents’ education can’t possibly affect your outcomes except through your own education!). It was the wild
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Lars Pålsson Syll considers the following as important: Statistics & Econometrics
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In search of identification — instrumental variables
We need relevance and validity. How realistic is validity, anyway? We ideally want our instrument to behave just like randomization in an experiment. But in the real world, how likely is that to actually happen? Or, if it’s an IV that requires control variables to be valid, how confident can we be that the controls really do everything we need them to?
In the long-ago times, researchers were happy to use instruments without thinking too hard about validity. If you go back to the 1970s or 1980s you can find people using things like parental education as an instrument for your own (surely your parents’ education can’t possibly affect your outcomes except through your own education!). It was the wild west out there…
But these days, go to any seminar where an instrumental variables paper is presented and you’ll hear no end of worries and arguments about whether the instrument is valid. And as time goes on, it seems like people have gotten more and more difficult to convince when it comes to validity. This focus on validity is good, but sometimes comes at the expense of thinking about other IV considerations, like monotonicity (we’ll get there) or even basic stuff like how good the data is.
There’s good reason to be concerned! Not only is it hard to justify that there exists a variable strongly related to treatment that somehow isn’t at all related to all the sources of hard-to-control-for back doors that the treatment had in the first place, we also have plenty of history of instruments that we thought sounded pretty good that turned out not to work so well.
Nick Huntington-Klein’s new book on how to use observational data to make causal inferences is superbly accessible. Highly recommended reading for anyone interested in causal inference in economics and social science!