Cause and Effect - An Introduction
Exam number: 6099
Semester: from 4th semester (Schwerpunktbildung)
Duration of the module: One semester
Form of the module (i.e. obligatory, elective etc.): Elective
Frequency of module offer: Each winter semester
Prerequisites: Successful completion of basic courses Statistik / Statistics.
Maximum number of students: Access to Stata in the computer lab is a prerequisite, so the maximum student number will be determined by the number of desks available, in line with covid-19 precautions.
Applicability of module for other study programmes:
Obligatory or elective in other study programmes. For further information check regulations of the study programme.
Person responsible for module: Prof. Dr. Felix Weinhardt
Name of the professor: Prof. Dr. Felix Weinhardt
Language of teaching: English
ECTS-Credits (based on the workload): 6
Workload and its composition (self-study, contact time):
Contact time (lecture, tutorials, seminar etc.) 60 h; self-study: 120 h
Contact hours (per week in semester): 4
Methods and duration of examination:
Successful completion of two seminar papers / term papers of five pages each (text only), and presentation of the results of the work.
Emphasis of the grade for the final grade: Please check regulations of the study programme
Aim of the module (expected learning outcomes and competencies to be acquired):
(a) ability to analytically think about causal claims made by others
(b) ability to critically evaluate empirical support for such claims
(c) knowledge about (some) methods and the importance of research design
(d) ability to conduct and program the code for small own studies using a widely-used software package.
(a) ability to critically evaluate statements made by others
(b) literature search
(c) program/software coding
(d) exposure to academic articles
(e) writing skills
(f) presentation skills
Contents of the module:
Every day claims are made about causes of things in an attempt to answer some of the great questions of our time. This course offers students a positive approach to think about causality. Students learn about different methods and research design to identify causality in the real world. While theory plays an important part, the course is “hands-on”. Students will examine properties and implement different methods using the statistical software Stata. Topics covered are: potential outcomes, directed acyclic graphs (DAGs), the value of experiments, hypothesis testing with and without distributional assumptions, OLS regression techniques and pitfalls, IV estimation.
Teaching and learning methods:
Lecture, tutorial, self-studies
Special features (e.g. percentage of online-work, practice, guest speaker, etc.):
Lectures and material will be delivered online, mostly via moodle and video clips, with on-site seminar sessions in the computer lab, if possible. Students are expected to work on take-home exercises throughout, and to present and discuss their results in the (online-) seminars.
Literature (compulsory reading, recommended literature):
The main textbooks are, in this order:
(a) Wooldridge: Introductory Econometrics, A Modern Approach,
(b) Angrist and Pischke: Mastering Metrics and
(c) Cunningham: The Mixed Tape.
The course will draw on a wide range of additional materials, including video clips (also produced by others) and academic articles. The textbooks are available online or via the library.
Registration in Moodle Viadrina required.