GESIS Training

GESIS Spring Seminar 2021

Causal Inference

March 01 - March 19, 2021, Cologne, Germany

The GESIS Spring Seminar (formerly ZA Spring Seminar) offers three consecutive one-week courses in advanced methods of quantitative data analysis for Social Scientists. It has been taking place in Cologne annually every spring since 1972. The Spring Seminar 2021 provided in depth knowledge of causal inference in the social sciences. It enabled participants to apply state of the art  techniques to their own research projects and provided participants with the opportunity to discuss their own research with interested colleagues and foremost experts.

March 01 - 05, 2021

Lecturer:

Asst. Prof. Dr. D.J. Flynn, Spain

Short Course Description:

This course examines lab, survey, and field experimental methods for causal inference. After providing an overview of the essential aspects of experiments, the course focuses primarily on common threats to inference that arise in experimental settings and how to avoid them. Selected topics will include theory testing, treatment design, estimation of heterogeneous treatment effects, convenience sampling and generalizability, preregistration, and ethics. The course will conclude with an applied session in which students design their own experiments, write a pre-registration, and program their experiments in Qualtrics (if applicable). Students will then peer review each other's designs.

March 08 - 12, 2021

Lecturers:

Dr. Krisztián Pósch, United Kingdom

Thiago Oliveira, United Kingdom

 

Short Course Description:

There is a growing expectation from governmental, and non-governmental organisations for social scientists to produce research which are capable of assessing the impact of certain changes to policies or the emerging effects of interventions. Yet, it is often difficult to analyse such changes when traditional experiments cannot be carried out either on the grounds of feasibility or due to ethical constraints. This course will provide participants with a toolkit to analyse so-called 'quasi-experimental' designs and draw causal conclusions using observational datasets.

 

We have put together a course focused on addressing practical considerations including when certain designs can be credibly employed and how the emerging results can be interpreted. In particular, the five most commonly used families of methods will be discussed: matching, difference-in-differences, instrumental variables, causal mediation, and regression discontinuity designs. We are to demonstrate each of these approaches by discussing existing applications from across the fields of the social sciences. We will also provide a tutorial for the 'R' statistical software and share with the participants the code for the methods covered by the course. We are also going to provide ample opportunity for participants to discuss their research plans, ask for advice regarding their own data, and recommend cutting-edge methods to address their research questions.

March 15 - 19, 2021

Lecturers:

Asst. Prof. Dr. Michael Knaus, Switzerland

Gabriel Okasa, Switzerland

Short Course Description:

Participants of this course will learn and apply recent Causal Machine Learning methods to analyse effects of either experimental or observational interventions. Causal Machine Learning combines two mature fields in data analytics. On the one hand, the field of Machine Learning advanced our ability to detect correlational pattern in data, which is important to form high-quality predictions. On the other hand, the field of Causal Inference advanced our knowledge about how to assess the effects of interventions, which is essential for high-quality decision making. The promise of Causal Machine Learning is to deliver the best of both worlds to draw (more) reliable and more informative causal inference.

This course will focus on tools that are already mature in the sense that they are easy to implement for practitioners in the software R and covers three major topics:

  • Estimation of heterogeneous effects for experimental data
  • Estimation of average and heterogeneous effects for observational data
  • Policy learning from experimental or observational data

The final day will also discuss how these methods extend to other research designs and questions like difference-in-differences, instrumental variable and mediation analysis.

The course will be based on three pillars to teach the new methods: (i) lecture based introduction of the theoretical concepts, (ii) getting to know the methods with toy synthetic data in R notebooks that are provided by the lecturer, (iii) supervised application to provided or own datasets.