Scientific Coordination
Dr.
Marlene Mauk
Tel: +49 221 47694-579
Marlene Mauk
Tel: +49 221 47694-579
Administrative Coordination
Angelika Ruf
Tel: +49 221 47694-162
Tel: +49 221 47694-162
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Week 1: Causal Inference and Experiments
Lecturer(s):
Asst. Prof. Dr. D.J. Flynn
Date: 01.03 - 05.03.2021 ics-file
Location: Online via Zoom / Course language: english
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.
Keywords
Experiments, surveys, causal inference, validity, preregistration, Data Analysis, R, Spring Seminar, 1 Week, Online, Advanced
Target group
Participants will find the course useful if they are interested in:
- Designing, implementing, and/or analysing experiments that take place in lab, survey, or field settings
- Becoming more critical consumers of empirical research that purports to demonstrate causality
- Evaluating the effectiveness of policies or programs on important outcomes in politics, public health, economics, or other fields
Learning objectives
By the end of the course participants will:
- Design valid lab, survey, and field experiments to test causal hypotheses
- Be more critical consumers of academic research in a wide range of fields that purports to make causal inferences
Organisational Structure of the Course:
Classes will feature a mix of lecture, tutorials, and hands-on exercises. The lectures will have an applied focus: the instructors will highlight key concepts that are necessary to design, implement, and analyse experiments. The hands-on sessions will ask students to design their own experiments from a research area of their choice. Specific hands-on activities will include writing a pre-registration, which includes specifying treatment, hypotheses, logistical choices, and so on; programming a survey experiment in Qualtrics; and peer reviewing other students' designs. Lecturers will be available throughout the applied sessions to answer questions and offer support.
Prerequisites
Software requirements
Students should bring their own laptops with R or RStudio already downloaded.
