30 June 2006
We've talked a lot on this blog about evaluating the quality of matching solutions when applying these matching for preprocessing, and in some of these discussions I've previewed and referenced arguments from a paper I was working on with Kosuke Imai and Liz Stuart. We have finally finished the paper. For anyone interested, you can get a copy here. The abstract follows. Comments welcome!
Matching methods are widely used to adjust for nonrandom treatment assignment when making causal inferences. In numerous articles across a diverse variety of academic fields that use matching, researchers evaluate the success of the procedure by conducting hypothesis tests, most commonly the t-test for the mean difference of each of the observed covariates between the matched treated and control groups. We demonstrate that these hypothesis tests are fallacious and discuss better alternatives.
28 June 2006
The noted Texas redistricting case, known politically for its role involving Tom DeLay and academically for the amici curiae brief filed by Gary King, Andrew Gelman, Jonathan Katz and Bernard Grofman, was ruled on by the Supreme Court today. In short, the party-based gerrymandering was not a problem - nor was the fact that it was done off the traditional calendar - but the composition of districts involving the dilution of Hispanic voters was. The court has ordered that those irregular districts be redrawn. (Note: only the composition of District 23 was considered to be in violation of the Voting Rights Act, but you obviously can't redraw one district without affecting another.)
The nature of this ruling should surprise no one involved in Jim Greiner's Quantitative Social Science and Expert Witnesses class.
18 June 2006
A friend emailed this to me;apparently the teaching assistants at the University of Oregon have creative as well as statistical talents. It's pretty funny. Perhaps every intro to statistics class could begin with a showing... video here