GESIS Training Courses

Scientific Coordination

Sebastian E. Wenz
Tel: +49 221 47694-159

Administrative Coordination

Jacqueline Schüller
Tel: +49 0221 47694-160

Short Course A: Using Directed Acyclic Graphs for Causal & Statistical Inference

Online via Zoom
Course duration
09:00-11:00 CEST
13:00-15:00 CEST
General Topics:
Course Level:
Software used:
Students: 200 €
Academics: 300 €
Commercial: 600 €
Additional links
Lecturer(s): Dr. Julian Schuessler

About the lecturer - Dr. Julian Schuessler

Course description

Please note: This Online-class is taught online only-live and in real time. Recordings will not be available.
This online short course uses causal graphs (or “directed acyclic graphs”, DAGs) as a remarkably simple, yet general and powerful framework to describe and discuss a large set of problems that empirical social scientists need to tackle. Is my question of interest descriptive or causal? How can I communicate my assumptions effectively to others, and can I test them? How can I tell correlation from causation? How do I choose control variables for my regression models? After discussing how DAGs can be used to answer these foundational questions, the course also covers basics of causal interaction and effect heterogeneity, causal mediation, nonresponse/selection bias (and adjustments for it) and, if time permits, instrumental variables and panel data analysis from a DAG perspective.
For additional details on the course and a day-to-day schedule, please download the full-length syllabus.

Target group

Participants will find the course useful if:
  • they are interested in causal questions and want to understand the assumptions associated with regression control, mediation analysis and instrumental variables better:
  • they are interested in non-causal questions, want to use data suffering from nonresponse, and want to understand how to use causal assumptions in this case.

  • Learning objectives

    By the end of the course participants will:
  • know how to use causal graphs to visualize causal assumptions, define quantities of interest, and to determine testability of assumptions via d-separation;
  • know how to graphically determine identification of causal and descriptive quantities like average causal effects, causal interaction, effect heterogeneity, natural direct and indirect effects, and population distributions from data with nonresponse;
  • know under what graphical assumptions instrumental variable and panel data analysis typically operate;
  • will have some basic knowledge about how all of this relates to implementation in standard statistical software.
    Organizational Structure of the Course
    This short course throughout will change between short lecture-style inputs and individual or small-group hands-on exercises supervised by the lecturer and a teaching assistant (4hrs/day). Participants are encouraged to bring their own research ideas to develop them further using the material from the class. The lecturer will also be available for individual consultation in the afternoons.


    Participants should be willing to learn and use formal reasoning and must have at least Bachelor-level knowledge of statistics. Basic knowledge of R is helpful.
    Software and Hardware Requirements
    We will briefly discuss some elements of the R packages “dagitty”, “sensemakr”, “mediation”, “AER”, “estimatr”, “PanelMatch”. Most of the course will not depend on using R. For those who have never used R, here are installation instructions and a short introductory video:
  • Downloading R and RStudio:
  • For those who have never used R before (18 mins):
  • Basics of simulation in R (15 mins):