This previous weekend found me and 60 other people attending the St. Louis Area Methods Meeting (SLAMM). This is the 3rd SLAMM I have attended, and the quality of the conference is consistently high. This year we had 4 speakers on Friday, which are the subject of this post. The conference also included graduate student speaker presentations on Saturday morning, which will be the subject of a second article.
The first speaker on Friday was Andrew Martin of Washington University reprising his talk of the previous year on The Supreme Court Database. The database is a ever growing collection of coded United States Supreme Court decisions. At the moment, the comprises two phases of the court history (from inception to the mid-1800’s and from the 1950’s to present).
This year, Martin gave us a peak behind the curtain at some of the more technical aspects of the implementation. Specifically, some details on their use of rApache, an Apache module that ties R to HTTP requests. While I am eager to have social scientists place their data on the web and integrate it with search and analysis tools, the talk highlighted to me the importance security when writing web front ends. From a subsequent email conversation with Andrew Martin, I am glad to report that the SCDB is working hard on security. Other projects should copy this aspect of the SCDB as well as its excellent feature set.
The second speaker of the day, Jong Hee Park of the University of Chicago, presented new techniques for intervention analysis. Given a time series data set and an intervention at a specific point, how can we compare the post-intervention data with a world in which the intervention did not occur? Additionally, while we may know the exact date of an intervention, the actual impact may come either earlier later (if agents anticipate the intervention in the first case or if there a lag between intervention and results). To this end, Park employs a Bayesian change-point model. In the simplest case, this modeling becomes a robustness check: the method detects the change when we think it will occur. In more interesting cases, the change point analysis points to an earlier or later location for the regime change.
After we detect/confirm the change point, we can then turn to predicting the results in the absence of the actual intervention. Park presented a method that was similar to the “synthetic control” technique of Abadie, Diamond and Hainmueller. In essence, we use units that did not receive the treatment to create a projection of our treatment unit without the intervention.
The discussion of the paper was handled in a typically lighthearted fashion by Jake Bowers, getting several laughs from the audience. Bowers prompted us to think about the plenitude of ways in which we might summarize the effect of an intervention (i.e., not just a mean change to stationary process) or might fit our prediction curves.
The third Friday talk was given by Minjung Kyung of Washington University. Kyung is in the mathematics department at WUSTL, and the talk suffered from the unfamiliarity of political scientists with mathematicians and vice-versa. The subject of talk was spline with a Bayesian technique that is less computationally intensive than previous techniques. While the talk was motivated by the problem of analyzing “synthetic data,” I was never entirely clear why a spline approach was required for data that has passed through an obfuscation procedure.
The final Friday talk came from John Jackson of the University of Michigan. Jackson is trying to extend individual models of vote choice to aggregate models and measurements of partisanship. In other words, can we develop individual models of learning and attachment that translate to the larger trends we observe in aggregate data. While I think this approach is interesting, I am concerned that it is premised on assumption of individuals as atoms, not influence and learning from each other. Politics is an inherently social behavior. Neighbors, family members, door-to-door canvassers all interact to reinforce and challenge our political positions. While we would observe these effects in the aggregate, they are not captured by an individual based model of partisanship at the lower level.