GRA 6845 Causal Inference with Big Data
GRA 6845 Causal Inference with Big Data
In the 21st century, information is created, digitized and stored at unprecedented rates. The access to high-dimensional large data sets – “Big Data” – has opened up new possibilities for business analytics and economic research. Massive datasets alone are, however, insufficient to answer fundamental questions within business and economics. Using the potential outcome framework, we explore various methods useful for causal inference in the Big Data era. We discuss the promise and pitfalls of large-scale experimentation and consider empirical applications relevant for business and policy analysis.
After having completed this course, students should be familiar with the potential outcome framework and microeconometric methods useful for answering “what if” questions using Big Data. Students learn the distinction between causal models and predictive models.
Students learn how large data sets should be used for decision making. Students receive hands-on experience by replicating and extending published research papers.
Students learn how to critically discuss empirical research of the Big Data era. They are trained to pay particular attention to the underlying assumptions of the methods used and the empirical implementation.
The course covers the following topics:
- What is big data?
- The potential outcome framework
- Regression and matching
- Large-scale experimentation
- Treatment effect heterogeneity
- False positives and p-hacking
- Publication bias
- Regression discontinuity designs
- Supplementary analysis
- Data visualization
- Feature engineering and feature learning
- Introduction to image analysis
- Introduction to text analysis
Students are expected to participate actively in class and must present some of the articles on the reading list. Student presentations count for 20% of the grade.
Please note that while attendance is not compulsory in all courses, it is the student’s own responsibility to obtain any information provided in class.
All parts of the assessment must be passed in order to get a grade in the course.
All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.
| Assessments |
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Exam category: Submission Form of assessment: Written submission Weight: 80 Grouping: Group/Individual (1 - 3) Duration: 1 Semester(s) Comment: Term paper Exam code: GRA68452 Grading scale: ECTS Resit: Examination when next scheduled course |
Exam category: Activity Form of assessment: Presentation Weight: 20 Grouping: Group (1 - 4) Comment: Presentation Exam code: GRA68453 Grading scale: ECTS Resit: Examination when next scheduled course |
All exams must be passed to get a grade in this course.
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.