I have recently published a peer-reviewed article as an independent researcher: "Marginal Structural Models to Estimate Causal Effects of Right-to-Carry Laws on Crime". I used modern causal inference methodology from epidemiology to examine the effect of Right-to-carry (RTC) laws, that allow the legal carrying of concealed firearms for defense in the US. I fitted marginal structural models (MSMs), using inverse probability weighting (IPW) to correct for criminological, economic, political and demographic confounders. Results indicate that RTC laws significantly increase violent crime by 7.5% and property crime by 6.1%. RTC laws significantly increase murder and manslaughter, robbery, aggravated assault, burglary, larceny theft and motor vehicle theft rates. Applying this method to this topic for the first time addresses methodological shortcomings in previous studies such as conditioning away the effect, overfit and the inappropriate use of county level measurements.

I have published the R package "elections", including a dataset with the outcomes of the USA presidential election, as well as possible predictors. I am planning to fit predictive models to these data, but haven't gotten around to it yet. However, I am very happy that the package has already been used for an interesting analysis and blog post by Ryan Ferris of Bellingham Politics and Economics.

First and foremost, I feel much sympathy for people who have lost their job. Furthermore, I find the cyclical nature of the unemployment level in the USA highly fascinating. And the USA seems to be doing well right now:

 (Data source here).

Recently I received a question from a researcher at the World Trade Center Health Registry (WTCHR). The WTCHR is a prospective cohort study of the physical and psychological effects of the 9/11 terrorist attacks. I was asked how to prepare a dataset for inverse probability of censoring weighting (IPCW) with the R package ipw. In response I wrote a tutorial with R code and simulated example data.