November 2009

Climate change and conflict in Africa

A paper just published in PNAS finds that armed conflict in Africa in recent decades has been more likely in hotter years, and projects that warming in the next twenty years will result in roughly 54% more conflicts and almost 400,000 more battle deaths. This is an important paper and it probably will attract significant attention from the media and policymakers. I think it's a good paper too -- seems fairly solid in the empirics, nice presentation, and admirably forthright about the limitations of the study.

Everything about causal inference in 40 pages

Judea Pearl describes his new article Causal inference in statistics: An Overview as "a recent submission to Statistics Survey which condenses everything I know about causality in only 40 pages." That seemed like a bold claim, but after reading it I'm sold. I don't come from Pearl's "camp" per se, but I found this a really impressive overview of his approach to causation.

breast cancer, rare diseases, and bayes rule, revised

Happy Thanksgiving!

Last Thursday, I posted about the recent government recommendations regarding breast cancer screening in women ages 40-49. At least one of you wrote me to say that one of my calculations might have been slightly off (they were), and so I did some more investigation on this issue, as well as on new recommendations on cervical pap smears. (Sorry --it took
me a few days to get around to all of this!)

Violations of SUTVA

Network methods and methods for causal inference are popular areas of research in social sciences. Often they are considered separately due to a fundamental difference in their basic assumptions. Network methods assume that individual units are interdependent, that one network member's actions have consequences for other members of the network. Methods for causal inference, in contrast, often rest on the Stable Unit Treatment Value Assumption (SUTVA).

Dynamic Panel Models

I have been toying around with dynamic panel models from the econometrics literature and I have hit my head up against a key set of assertions. First, a quick setup. The idea with these models is that we have a set units which we measure at different points in time. For instance, perhaps we survey a group of people multiple times in the course of an election and ask them how they are going to vote, do they plan to vote, how do they rate the candidates, etc. We might then want to know how these answers vary over time or with certain covariates.

Here is a typical model:

Choosing variances in general linear models

Today I'm going to talk about a particular problem from my own research and will outline a method for choosing variances in general linear models (GLMs), but I am also asking a question.

The standard setup of GLMs is (roughly) the following. One hypothesizes that the conditional mean of the outcome variable (y), E[y|x], can be expressed as a function of a linear predictor x'b, or: