I’m Thomas Wiemann, Economics PhD student at UChicago.

My primary research interests lie in econometrics. Recent projects focus on inference with many categorical variables, the zero-market-share problem in discrete choice demand estimation, and identification of causal effects of macroeconomic variables on microeconomic outcomes. See Research for details & drafts.

I also greatly enjoy working on computational projects. My co-authors and I recently released the first package for double/debiased machine learning in Stata. Other in-progress work I contribute to inlcude a package for double/debiased machine learning in R, and the python library scriptflow for asynchronously scheduling scripts on computing clusters. See Computing for details.

One highlight of my PhD was to design and lecture an undergraduate course in econometrics. The course focused on the three distinct tasks arising in the analysis of causal questions (see Heckman and Vytlacil, 2007): Definition, identification, and estimation of causal parameters. See Teaching for the course materials.

Don’t hestitate to reach out if any our interests overlap: I’m sure we’ll find something exciting to chat about! See CV for my contact details.