If you are a PhD student in the social sciences, you may want to take
A quantitative methods sequence focused on causal inference. Jennie Brand and I teach sociology 212 A, B, and C at UCLA. I have taught B and C. These courses introduce causal and descriptive estimands, nonparametric identification with Directed Acyclic Graphs, and estimation by statistical and data science approaches. My materials are here: https://ilundberg.github.io/soc212b/.
Event history analysis. SOC 213B covers descriptive survival analysis for time-to-event data, as well as causal inference in event history settings: https://ilundberg.github.io/eventhistory/.
Social Data Science (SOC 114) welcomes all students regardless of coding and statistical background. We learn visualization, how to define and identify descriptive and causal estimands, and estimation by selected statistical and machine learning approaches.
Below are links to course materials for courses I taught in the past.
At Cornell University,
Causal Inference (undergraduate, co-taught with Sam Wang).
Fall 2023. causal3900.github.io/fa23
Studying Social Inequality with Data Science (undergraduate)
Spring 2024. info3370.github.io
Spring 2023. info3370.github.io/sp23
Causal Inference in Observational Settings (PhD seminar)
At Princeton, I was a PhD student teaching assistant in the quantitative methods sequence for PhD students. Materials I designed include handouts and slides on generalized linear models, random variables, likelihood inference, binary outcome models, duration models, and missing data.