GESIS Training Courses

Scientific Coordination

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

Administrative Coordination

Jacqueline Schüller
Tel: +49 0221 47694-160

Course 3: Causal Inference Using Survey Data

Cologne / Unter Sachsenhausen 6-8
Course duration:
Mo: 10:00-17:00 CEST Tu-Th: 9:00-17:00 CEST Fr: 9:00-17:00 CEST
General Topics:
Course Level:
Software used:
Students: 500 €
Academics: 750 €
Commercial: 1500 €
Additional links
Lecturer(s): Heinz Leitgöb, Tobias Wolbring

About the lecturer - Heinz Leitgöb

About the lecturer - Tobias Wolbring

Course description

This course will introduce participants to the concepts and methods of causal inference and causal modeling in the social sciences. It will highlight the relevance of research design, analytical methods and their systematic combination to optimize the validity of causal inferences drawn from empirical studies. Participants will learn the key principles and techniques of causal inference, including potential outcomes, counterfactuals, and causal graphs, and will get to know the experimental approach to causality. Building on existing knowledge concerning linear regression modelling and research design, the course will then cover key methods of causal modeling using survey data, such as fixed effects panel models, matching, difference-in-differences, regression discontinuity, and instrumental variables. Throughout the course, participants will apply these concepts and methods in hands-on sessions to real-world examples in the social sciences. The application will be conducted with the statistical software package Stata. The course will also touch upon advanced topics such as effect modification, reverse causality, measurement issues, and data quality. By the end of the course, participants will have the skills and knowledge to design, conduct, and interpret causal inference studies in the social sciences. They will be able to engage with the contemporary literature of causal inference and identify state-of-the-art methods which might be most relevant to their specific research question.
A detailed syllabus with course times and literature will soon be available for download here.

Target group

Participants will find the course useful if they:
  • have a background in the social, behavioral or economic sciences (economists, political scientists, sociologists, criminologists, psychologists, etc.).
  • are interested in methods for causal inference based on experimental and/or observational data, especially panel data.
  • have a firm knowledge in linear regression modelling.
  • are motivated to apply the concepts and statistical approaches in hands-on sessions.

  • Learning objectives

    By the end of the course participants will:
  • have a good understanding of the potential outcome framework, causal diagrams, and the counterfactual way of thinking.
  • be capable of designing their own studies to derive causal estimates in observational settings.
  • acquire an in-depth understanding of and the skills to carry out five family of methods: fixed effects models, matching, difference-in-differences, instrumental variables, and regression discontinuity design.
  • become familiar with interdisciplinary applications of the methods covered by the course.
  • be able to engage the contemporary literature of causal inference and identify state-of-the-art methods which might be most relevant to their specific research question.
    Organizational structure of the course
    The course will be split into a three-hour morning (9:00-12:00) and a three-hour afternoon session (13:30-16:30), including coffee breaks. In order to secure a close link between the learning and the application of contents, we will switch between lecture format (~50%) and hands-on exercises, tutorials, or lab sessions (~50%) in a flexible way. In addition to shorter exercises, a selected number of more in-depth assignments will be provided which participants solve in groups of 2-3. These include the application of causal inference methods to estimate effects based on existing datasets using Stata. Lecturers will be available for individual consultations to support work on group assignments and to facilitate discussions within groups.
    We will offer an office hour for discussing individual questions and providing input and support for participants' projects.


  • Knowledge of basic statistical concepts, including the principles of linear and binary logistic regression
  • Background in statistical software, preferably Stata
  • Basic understanding of designing quantitative studies
    Software and hardware requirements
    Participants will need to bring a laptop computer with a recent version of Stata (13 or higher) installed to successfully participate in this course. Stata short term licenses will be provided by GESIS for the duration of the course if needed.