1. Causality and statistical learning (Wed 12 Feb 2014, 16:00, at University of Bristol):
Causal inference is central to the social and biomedical sciences. There are unresolved debates about the meaning of causality and the methods that should be used to measure it. As a statistician, I am trained to say that randomized experiments are a gold standard, yet I have spent almost all my applied career analyzing observational data. In this talk we shall consider various approaches to causal reasoning from the perspective of an applied statistician who recognizes the importance of causal identification, yet must learn from available information.
This is a good one. They laughed their asses off when I did it in Ann Arbor. But it has serious stuff too. As George Carlin (or, for that matter, John or Brad) might say, it’s funny because it’s true. Here are some old slides, but I plan to mix in a bit of new material.
2. Theoretical Statistics is the Theory of Applied Statistics (Thurs 13 Feb 2014, 17:00, at Imperial College London):
The audience will get to vote on which of the following talks they’d like to hear:
- Choices in statistical graphics
- Little Data: How traditional statistical ideas remain relevant in a big-data world
- Weakly informative priors
Actually, I’d be happy to give any of my prepared talks (except I don’t want to repeat the talk from Bristol). What happened was that I was paranoid on what to speak about. On one hand, the applied stuff is of broadest interest, and even theory people like to hear about what’s going on in American politics. On the other hand, I don’t want to get the reputation as a softie, and I do do technical things from time to time. So I thought I’d throw the choice at the audience. That way, if they pick something technical, I know they actually want to hear it, and if they pick something softer, at least it’s clear that it’s their choice. All three of the above are fine (really, I should add some material to talks #2 and 3 above, maybe I’ll do some of that on the train).