Causal Graphs Seminar, Summer 2018

This course covers the modern theory of causal inference from a social science perspective. We approach this topic by using causal graphs, which are extremely general and powerful theoretical devices to solve causal problems; at the same time, they are easy to understand. After the course, participants should better understand cutting-edge research, methodological discussions, and be able to improve the quality of their own research. Apart from that, the tools discussed in this course are also increasingly used by private companies. We start with a refresher of some basic probability theory. We then use causal graphs to understand problems like estimating causal effects, choice of control variables, selection bias, and testing causal assumptions, in nonparametric and in linear graphs. We then deal with effect heterogeneity and causal interactions. We conclude the first part by introducing counterfactuals, their connection to graphs and structural equations, and their application to decision-making problems. Throughout, we will discuss examples from research, business, and daily life. After this foundational part, we will find it easy to delve into two more advanced topics, instrumental variables and the analysis of causal mechanisms. Here, we focus more on discussing research applications. Towards the end of the course, participants have time to discuss how to use the tools covered in this course for their own research. Link to ZEUS.

Literature: Pearl, Judea, Madelyn Glymour, and Nicholas P. Jewell. Causal Inference in Statistics: A Primer. John Wiley & Sons, 2016.

This course was awarded the "Causality in Statistics Education Award" 2019 by the American Statistical Association.