´╗┐´╗┐ GESIS Training Courses

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

Administrative Coordination

Angelika Ruf
Tel: +49 221 47694-162

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

Online via Zoom / Time: 09:00-11:00 & 13:00-15:00 (CEST)
General Topics:
Course Level:
Software used:
Students: 160 €
Academics: 240 €
Commercial: 480 €
Additional links
Lecturer(s): Julian Schuessler

About the lecturer - Julian Schuessler

Course description

[This is a 12 hour class.]
This course uses causal graphs (or “directed acyclic graphs”) 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 mediation, instrumental variables, nonresponse/selection bias (and adjustments for it), and (if time permits) panel data analysis from a “graphical” perspective.

Target group

Participants will find the course useful if:
  • They are interested in causal questions and want to understand the assumptions associated with various “causal methods” better
  • They are interested in non-causal question, want to use data suffering from nonresponse, and want to understand why causal assumptions are necessary 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. After each class, participants are expected to do some homework exercises (1hr/day). The lecturer will also be available for individual consultation in the afternoons (1hr/day).


    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 use base R for simulation, and will briefly discuss some elements of the R packages “dagitty”, “simcausal”, “sensemakr”, “mediation”, “AER”, “estimatr”, “PanelMatch”,