Regression is the core of my statistics and program evaluation/causal inference courses. As I’ve taught different stats classes, I’ve found that one of the regression diagnostic statistics that students really glom onto is . Unlike lots of regression diagnostics like AIC, BIC, and the joint F-statistic, has a really intuitive interpretation—it’s the percent of variation in the outcome variable explained by all the explanatory variables.

Since my last two blog posts on binary and continuous inverse probability weights (IPWs) and marginal structural models (MSMs) for time-series cross-sectional (TSCS) panel data, I’ve spent a ton of time trying to figure out why I couldn’t recover the exact causal effect I had built in to those examples when using panel data. It was a mystery, and it took weeks to figure out what was happening.

In my post on generating inverse probability weights for both binary and continuous treatments, I mentioned that I’d eventually need to figure out how to deal with more complex data structures and causal models where treatments, outcomes, and confounders vary over time.

My program evaluation class is basically a fun wrapper around topics in causal inference and econometrics. I’m a big fan of Judea Pearl-style “causal revolution” causal graphs (or DAGs), and they’ve made it easier for both me and my students to understand econometric approaches like diff-in-diff, regression discontinuity, and instrumental variables.

Now that I’m on the tenure track, I’ve been looking for a way to keep track of my different research projects so I can get them all finished and published. Matt Lebo’s “Managing Your Research Pipeline” presents a neat way of quantifying and tracking the progress of your research, and I recently adopted it for my own stuff. I even made a fancy R Markdown + flexdashboard dashboard to show the status of the pipeline interactively.

(See this notebook on GitHub) A year ago, I wrote about how to use R to solve a typical microeconomics problem: finding the optimal price and quantity of some product given its demand and cost. Doing this involves setting the first derivatives of two functions equal to each other and using algebra to find where they cross.

(See this notebook on GitHub) Taking a sample from two groups from a population and seeing if there’s a significant or substantial difference between them is a standard task in statistics.

I am so beyond thrilled to announce that I’ll be joining the Andrew Young School of Policy Studies at Georgia State University in Fall 2019 as an assistant professor in the Department of Public Management and Policy. I’ll be teaching classes in statistics/data science, economics, and nonprofit management in beautiful downtown Atlanta, and we’ll be moving back to the South. I am so so excited about this!