I’ve been reading a truly excellent book by Joshua Angrist and Jorn-Steffen Pischke called Mostly Harmless Econometrics: An Empiricist’s Companion. It’s not written for a general audience, but if you pulled an A- or better on a college-level econometrics course (and if you love Freakonomics), then this is the book for you. It should be required reading for anyone who is trying to write an applied dissertation. It is the rare book that captures the feeling of how to go about trying to attack an empirical question; and it does this by working through two or three dozen of the neatest empirical papers of the last decade (often coauthored by Angrist). It is also peppered with references to Douglas Adams‘s writing — so what’s not to like?
Here’s a fine example, in plain English, explaining how econometricians think about what they are doing:
[Something that distinguishes] the discipline of econometrics from the older sister field of statistics … is a lack of shyness about causality. Causal inference has always been the name of the game in applied econometrics. Statistician Paul Holland (1986) cautions that there can be “no causation without manipulation,” a maxim that would seem to rule out causal inference from nonexperimental data. Less thoughtful observers fall back on the truism that “correlation is not causality.” Like most people who work with data for a living, we believe that correlation can sometimes provide pretty good evidence of a causal relation, even when the variable of interest has not been manipulated by a researcher or experimenter. (p. 133)
The book backs up this assertion by teaching the reader to think carefully about what assumptions about the counter-factual are necessary to make a causal inference. I was thinking about the book a couple of weeks ago when reading a New York Times article discussing the college and law-school years of Supreme Court nominee Sonia Sotomayor. The article in the second paragraph claims that Judge Sotomayor “benefited from affirmative action policies.” To me, this is pretty clearly a causal claim and this claim is not well supported by the subsequent evidence in the article.
At least one relevant counterfactual question to ask is “What would have happened to Judge Sotomayor in applying to college and law school in a world without affirmative action?” We are told that Ms. Sotomayor was an honors student in high school and that she graduated near the top of her class in college. James A. Thomas, a former dean of admissions, concluded that “Ms. Sotomayor’s background had little role in her acceptance to [Yale Law] school.” This is hardly strong evidence for claiming that she was a beneficiary of affirmative action. The article shows that it is not just econometricians who can mistake correlation for causation. It is a mistake that a reader of Angrist and Pischke is less likely to make.
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@Caliphilosopher
2 – Of course the political process isn’t meritocratic. I was merely providing additional evidence, not cited in the post, to support the claim that she was helped by AA. After the admissions office, I think the student is in the best position to know whether or not they were given a leg up by AA.
3 – Again I’d point out the student is in a very good position to know how they stack up against their peers. Whatever the criteria for admission, Sotomayor believes she was helped by AA. I don’t see how I could possibly doubt her on that point.
Testimony has a context. Sotomayor was affirming that suppor fpr Affimative Actiont when she claimed to be a beneficiary of Afirmative Action at Princeton and Yale. Those Justices appointed for their exceptional legal expertise were seldom marginal admits to Law School.
@Special K
I think that your comments were furthest from missing the mark. I agree with you that the student would be in the best position to know, but I think that would be the case only if accurate grades were made public and non-anonymous.
@ jdiec
You mean to tell me that my very first comment wasn’t on the mark as far as causation in economics? I thought that it was rather relevant to the attempt to measure causal influences on social/economic events, which is much harder to do than merely physical or chemical events. There is plenty to be said regarding issues surrounding measuring causation in economics (methodological individualism, participant-observation, unreasonable assumptions about the model used to predict whether or not something is “caused”, etc.)
Darn you. Add one more book to the reading list. Thanks.
Always keep in mind that 108 of the 112 previous Supreme Count members were white men. I’m sure every one of them was a meritocratic decision.
Well I was C- so I guess I am excluded from this
Cheers!!
Joshua Angrist taught my Econometrics class last semester at MIT, and MHE was on the reading list. I dare say I did not read the whole thing, but I learned a ton from his class. He definitely knows his stuff! I recommend reading some of his papers too, especially if you’re into labor economics.
It does strike me as interesting that race is deemed NOT a suitable entry criterion for firefighters, but one can get away with nominating an hispanic woman for the Supreme Court even after leaking to the press that an hispanic female candidate would be politically expedient. One possible defense of race-based nominations to the Supreme Court might be that there are very few of them, and hence don’t impact employment statistics much.