Atlantic Causal Inference Conference
I recently returned from the Atlantic Causal Inference Conference. The conference is attended by an interesting blend of social science, biostatistics, epidemiology, and pure statistics scholars. My compliments to the organizers. There were panels and presentations on interference in randomized trials and dependence between units, matching as a tool against selection and heterogeneity, instrumental variables, and big think on making more credible causal inferences from observational data.
I presented some early, but exciting, work on testing models of spillover effects in randomized experiments. This work speaks to two audiences. First, experimenters afraid that interference between experimental units violates the assumptions of their statistical tools (specifically, the Stable Unit Treatment Value Assumption, or SUTVA for short). Second, this research offers new tools to researchers studying the effects of networks themselves. My ACIC poster (also available as a source Sweave document) introduces the basics of the work and demonstrates the technique on some simulated data, for which we know the truth. It also demonstrates new software for flexible randomization inference.
The informal theme of the conference was that we need to consider more than a single study or test to build a causal story. Multiple results that point to a common conclusion are stronger than a single indicator. Similarly, researchers should consider and evaluate alternative explanations directly (exemplified in the continual use and calls for sensitivity analyses across papers and panels). From the perspective of critiquing causal research, these points require specific, testable criticisms. In his keynote address, Sandr Greenland called on the audience to formalize these themes and make them accessible to the wider population of statistical consumers.
