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This post presents a simple explanation of the concept of “local average treatment effects” in the context of instrumental variables estimation.  I borrow shamelessly from the somewhat more advanced presentation in Imbens and Wooldridge’s lecture notes, which is a good place to look for further reading.

The basic idea underlying LATE is to acknowledge that different people (or different units more generally) generally have different causal effects for any given “treatment,” broadly defined.  It is common to talk about “the” causal effect of, say, education on earnings, or interest rates on growth, or pharmaceuticals on health, but if different people respond differently to education or to medical treatments and different countries respond differently to macroeconomic interventions, it’s not clear what we mean by “the” causal effect.  We can still talk coherently about distributions of causal effects, though, and we may be interested in estimated various averages of those causal effects.  Local average treatment effects (LATEs) are one such average.

For concreteness, let’s suppose the government decides to lend a hand to empirical researchers by implementing the following goofy policy: a randomly selected group of high school kids are randomized to get an offer of either $0 or $5,000 to acquire a college degree.  We wish to use this natural experiment to estimate “the” effect of getting a college degree on, say, wages.  We collect data on all these folks comprised of: a dummy variable Z_i which equals one if person i was offered $5,000 and zero if they were offered zero, a dummy variable D_i which indicates the student actually received a college degree, and wages, y_i.

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New server

This blog has moved servers.  Aside from aesthetic and minor functionality differences, everything should be as it was before.  The transition caused some issues with the mathematical typesetting, a few bugs may remain.

 

Moved.

This imaginatively titled blog was briefly quantecon.wordpress.com. You will not be subject to advertisements on this new site.

  1. Kevin Milligan on British Columbians voting down the HST. I await the median voter selecting Mr. Dwayne Elizondo Mountain Dew Herbert Camacho as our next Premier.
  2. Dan Hamermesh proposes making ugly people a protected group in civil rights legislation. See also his recent paper with Jason Abrevaya showing good looks cause happiness.
  3. Ridley Scott to direct new Blade Runner film.
  4. I think this is supposed to be a real paper, not Swiftian satire.
  1. My colleague David Giles points out that the usual test statistics in linear regression follow textbook distributions assuming normal errors when the errors are not normal but come from the class of elliptically symmetric distributions (which includes Student and logistic). I didn’t know that!
  2. Has everyone noticed that Stata 12 has been released?
  3. A failed attempt to mock regression analysis in the social sciences.
  4. Rejection letters from journal editors are somewhat more polite, but I’d prefer this.

Since what the world needs is one more blog, this is a blog about quantitative economics and other stuff that interests me.  Topics will include discussion of interesting new papers in economics or other disciplines, issues in statistical methodology, current events, or whatever else strikes my fancy.

Copyright © 2017 M. Christopher Auld